Corruption on the Cuyahoga

The Cuyahoga River runs through NE Ohio into downtown Cleveland emptying in Lake Erie.  Native Americans named the river "cuyahoga" because it was crooked — full of bends and turns — following a serpentine structure.

It was a fortuitous naming — "crooked" has been the theme for politics in the County called Cuyahoga.  In 2008, the FBI and IRS raided the County administration offices, and homes of some of the employees, to begin the long process of exposing the corrupt network of favors that controlled business within the County. This corrupt network followed the basic rule of all closed networks: "you have to buy in, to get in."

The network map below shows most of the network (132 people) that has been the focus of the federal probe.  A person is included in the network if they have been charged with a crime, or have been listed on an indictment/information or search warrant as having been "tied" to a suspect.  The nodes in red have been "charged" with a crime, the nodes in gray have no charges (as of January 9, 2012), and the nodes in black have been involved, but have passed away since the investigation started.  The three key nodes in the case (and in terms of network metrics) are hi-lited in blue — Frank Russo, J. Kevin Kelley and Jimmy Dimora. They are the central hubs in the network. The first two have already pleaded guilty and will testify against Dimora in the upcoming trial.

Most of the charged individuals have either plead guilty or have been found guilty in a jury trial.  A few contractors have been absolved of their charges by local juries.  Jimmy Dimora plead "not guilty" and will now face a federal racketeering trial in Akron, Ohio.

Below is the network graph of the witnesses for the Dimora trial made public by the presiding judge Sarah Lioi, and also printed in the Plain Dealer.  Jimmy Dimora is being tried together with co-defendent Michael Gabor — both hi-lited in pink.  The five key witnesses in the trial are all hi-lited in green.  People are linked if they were mentioned as tied in an indictment/information or search warrant.  We see how the witnesses are connected to the defendant.

Next we will look at the core of this conspiracy — see the social graph below.  We see that the core is broken into 2 clusters, with the central connector being Frank Russo.  He received the highest social network metric scores for both Power and Structural Holes.

Russo is holding the two clusters together.  As this very central figure, will he get the longest prison sentence for his crimes? We are sure judges don't do social network analysis, before sentencing.  Our social network metrics "predict" that Russo will get the longest sentence, followed by Dimora and Kelley, in that order.  Of course both Kelley and Russo have been cooperating with the investigators, so their sentences may be significantly affected by that.  We'll see.

In addition to hundreds of pages of legal documents (network data extracted by researcher Silvija Krebs), I used information from the Cleveland Plain Dealer which has been reporting on this investigation since day one. We will update the charts as we get new information. Thank you all!

Innovation happens at the Intersections

On December 3rd I attended part of the worldwide OpenData Hackathon, locally in Riga, Latvia.  A few dozen people were there and it was a good mix -- idea people and activists, data analysts, and coders/hackers (data extraction specialists).  The morning turned into a talkathon, but after Lunch we started digging for "interesting data" to play with.

Our first data set were the donations in 2010 and 2011 to various Latvian political parties from various individuals and companies.  After filtering out the smallest donations, we saw a familiar pattern -- most donors choose one party, and one party only, to donate to.  Several donors contributed to multiple parties -- spreading their bets (or "investments").  The donation pattern created a mostly hub-and-spoke network which is below. The green nodes are the parties, and the black nodes are the donors.  A black node is linked to a green node if a contribution above a certain amount was made.  Kind of a nice network for the holidays, eh?

Next we dug into the voting patterns of the Latvian parliament, the Saeima.  An early comment was that the data set would probably not show anything interesting — afterall, the parties keep a tight reign on the deputies, strongly encouraging everyone to vote similarly.  Being a long time student of social networks and emergent organization, I was not so sure.  I said, "Let's give a try and let the data speak"

Even though the data/results of each deputy vote is published on the Saeima web site, the data was not easy to extract for pattern-matching.  Raimonds Simanovskis and Jānis Baiza made a heroic effort and got the voting record data of the 11th Saeima.  Uldis Bojars ran the data through Python scripts to give us network data.

Our first few attempts at interesting visualizations were not successful.  Unfortunately the data contained many counts from procedural votes — where all deputies usually vote in the affirmative to move legislation along.  We had to filter the procedural votes out so that only the votes on substantial legislation remained. Once that was accomplished we were left with data that would expose voting patterns on important issues.  Like often in social network analysis, those who thought they knew the data, were surprised.  The deputies did not always vote as their parties instructed!  Some interesting political bedfellows emerged.  What was even more interesting where the patterns of the individual parties and where they end up on the network map.

We filtered the data to show the stronger, higher occurrence patterns.  The first pattern we saw was the Latvian/Russian split in the parliament.  The largely Russian Harmony Centre party was a an obvious cluster — they are the opposition party. The ruling coalition of Unity, Zatler Reform Party, and the National Alliance —all majority ethnic Latvian — formed an integrated cluster.  The remaining party, the Greens/Farmers had a choice — 1)isolation, 2)joining one of the two clusters, or 3)bridging the two clusters.  They have decided to be the bridge in the early months of the 11th Saeima.  

The network map of deputy voting patterns is below.  Two deputies are connected if they voted the same on many important pieces of legislation.  Harmony Centre are the bright red nodes on the right, the dark green nodes in the middle are the Greens/Farmers.  The darker maroon nodes are the National Alliance, the lighter purple nodes are Zatler's Reform Party and the light green nodes are Unity.  They are all colored by their branding colors.  The number of nodes shown does not add up to the total number of deputies in parliament — some of the deputies did not survive the high cutoff in this data set for number of votes.

The network map above shows the ruling coalition on the left side, with the near opposition in the center, and the far opposition on the right.  The ruling coalition and the far opposition do not have a pair of deputies voting alike.  The between party -- Greens/Farmers -- appear to be keeping the network from totally fragmenting.

We will allow the political experts in Latvia to explain the patterns in the above network — we can provide a copy of the network map with all of the deputy names.  How will this voting pattern affect a small country coming out of a deep economic recession? I for one, am happy not to see five disconnected clusters.  The bridging ties in the above network give me hope we can work toward a better Latvia for all.

This day of open data exploration was a perfect example of how innovation happens at the intersections!  People of different skills, perspectives, knowledge and goals came together around open government data and at the end of the day had formed an emergent network that was connected and moving forward, yet was not subverting individual talents and goals.  Like I always say:  Connect on your similarities and benefit from your differences!  

Maybe this mantra will play out in the Saeima also?

Thanks Steve!

In 1988, I programmed the first version of InFlow on an original Macintosh (with added memory), using Prolog.

When I bought that computer in 1984, I knew it would change my life. I just did not know how at that moment... the dots are connected now. Thanks Steve!

"You can't connect the dots looking forward;
you can only connect them looking backwards."

 Steve Jobs

P.S. Think Different

Circle of Influence

As the political season is now in full bloom, many of us are going to be looking at politicians and who influences them.

An easy way of doing this is looking at the "Circle of Influence" — a simple network diagram that reveals how money and favors flow in a clock-wise direction. A generic example from one of our projects, is illustrated below.



Starting at the top (12 o'clock) in a clockwise flow...
• Company A wants to obtain new contracts (without competing in the open market)
• Executive(s) from company A donate(s) to political party X
• Members of political party X vote to award contracts/legislation in favor of company A
• Company A receives a monetary benefit from new contracts
• green arrows show money flow, while red arrows show influence/favor

Using these circles of influence we can see how politicians are embedded in networks of indebtedness and favor.

An example of the flow of influence from the Cuyahoga County Corruption Probe is shown here. This flow of influence did not succeed for the company/executive seeking favor.

An interesting insight into influence flows is that the longer they are, the more advantageous they are... for those involved. The more distance (steps in the network flow) a company/executive can put between themselves and the legislation/contracts they want to influence the harder it is to show an association/pressure. Executives and politicains want to avoid the obvious quid pro quo — they want the plausible deniability of an indirect quid pro quo.

What flows of influence will you spot in the upcoming elections?

Who gets Attention on Twitter?

Recently I viewed my Klout "influence score" on Twitter. It was 57. Curious, I checked out my PeerIndex "influence score" on Twitter, it was 60. Hmm, are these two scores becoming similar, measuring the same stuff?

I occasionally look at these scores and noticed that they had changed in the last few weeks. Both of my scores had fallen in the last few weeks.

Had I grown less influential during my recent travel visiting clients?  I don't think so!

I had spent less time on Twitter, but that does not mean I am less influential today than at the beginning of the month. Hey folks at @klout and @peerindex... I have news for you!  Influence is not like a suntan. It is not dependent on daily exposure/activity on Twitter!!

Influence is not like a suntan, it does not change much based on *daily* exposure!

I looked up a few twitter friends/colleagues and noticed they had similar scores across Klout and PeerIndex also -- some where closer than others.

Next, we retrieved the Klout and PeerIndex scores for all people [~ 200] I follow on Twitter to see if there were any interesting patterns in this sample. Some of them had almost identical Klout and PeerIndex scores, some were not calculated by one or the other service, and some had divergent scores.

Which score more accurately gauges real influence on Twitter? Are either of these influence scores significantly better than the back-of-the-napkin Twitter metrics [LFR score] I described earlier? How precise are these scores?

I found both Klout, and PeerIndex scores on 177 of the 200 people I follow. Of course, there is an LFR score for everyone on Twitter -- it is easily calculated by looking at a person's Followers and Listed counts in their Twitter Profile.

Looking at all three scores we see some difference, but not much.


• Klout and Peer Index differ by an average of 13 across the 200 people I follow
• Klout and LFR differ by 15 on average
• Peer Index and LFR differ by 18 on average.

When I remove some of the outliers [less than 10% of the population] the difference between the three scores shrinks noticeably.

Does it really matter which score we use? How accurately can you measure something as nebulous as influence or attention? Is a several point difference between scores a significant delta?

Of course, the good news is you do not need to be popular to receive deserved attention!

I still prefer the LFR (List to Follower Ratio) score -- when looking at someone's Twitter profile, I can easily calc LFR in my head and make a quick judgement on whether to follow this person, or add them to a topic list.

To calculate LFR quickly, add a zero (0) to the Listed number and then divide that by the number of Followers, i.e. I have 4088 followers and appear on 483 Lists, my LFR is 4830/4088 = 1.18. A number > 1.00 means people are paying attention to you, a score approaching 2.00 means you have the focused attention of many! Of course, the good news is you do not need to be popular to receive deserved attention!

LFR finds us such Twitter gems as @VenessaMiemis (LFR=1.66), @zenext (LFR=1.69), @jhagel(LFR=1.72), and @twliterary(LFR=1.50), each is paid great attention to in their respective field, and on Twitter.

What other Twitter influence/attention metrics do you track?

UPDATE1: Interesting interview by Augie Ray with Azeem Azhar, CEO of Peer Index.
 I like Azeem's concept of "cheap"(i.e. following) and "expensive"(i.e. responding) activities on Twitter. I agree, it is more important to look at the expensive activities to gain a more realistic perspective of who/what is really important.   IMHO, the power of LFR is in the very expensive activity of creating and curating Lists on Twitter!

UPDATE2: I have created a LFR Twitter List, that anyone can follow, of people whose LFR > 1.  If your LFR is greater than 1 and you are not on the list, and you have more than 100 followers, let me know!

Ecosystem Wars

In the connected world of today companies compete not just on products, but on integrated ecosystems of cooperating products and services. Business today is a war between networks... reach, inclusion, attention, power, control, influence... all those network dynamics are in play.

Great article from Gizmodo about the war between the ecosystems of Apple, Google and Microsoft for internet supremacy: The Dogs of War: Apple vs. Google vs. Microsoft

Gizmodo provides the network diagram below to illustrate the intertwined battle amongst the three titans of the consumer internet.


I am a big fan of network visualization, but a spaghetti diagram is not always a good solution. Yet, early in my network analysis career, I also produced spaghetti diagrams of the internet industry!

Visualization should help us quickly see the pattern(s) in a complex dynamic. Is there another way to display the data so that it is easily understandable by a business reader?

I took the data from the Gizmodo diagram, added this week's acquisition of Skype by Microsoft, and a few other missing items, and came up with this alternate view of the internet ecosystem wars below.


Displaying network links as intersections on a Venn diagram clears up the picture quite a bit -- quickly showing where the three big players do, and do not, overlap. In terms of social network analysis, it is easier to see the structural equivalence (and difference) of the network players with the Venn diagram.

Making Buying Decisions via Social Network Analysis

I have an interest in the recent financial crisis (a.k.a. mortgage meltdown) so I am constantly looking for good reading material about the topic. This morning I was wondering... "What should I read next?" "Which one book will cover most of the angles of this topic?"

Rather than spend time reading reviews of the popular books on the topic, I followed my own suggestion. Using social network analysis [SNA], I created a book network around the topic of "financial crisis". The data was gathered from Amazon and the map of the most frequently mentioned "also bought" books is displayed above. The map does not rank books by sales volume, though it does show the most popular book on the topic of financial crisis.

The network map above shows how books were bought together and by the same customers: A-->B means that people who bought book A also bought book B. This book network helps us see which books are most influential and most integrated in this topic area. I am looking for ONE book to read on the subject, so I will be examining the "integration" scores of each book in the network.

After gathering the data and putting into my software, the network self organizes into two clusters. The cluster on the left contains mostly economic perspectives on the financial crisis and the books in the small cluster on the right contain more of a political perspective on the crisis. I am more interested in the economic dynamics of the financial crisis so I will focus on the cluster on the left.

Two books emerge at the top of the list of network integration scores [the nodes in the network map are sized according to relative integration scores] -- Too Big to Fail and All the Devils Are Here. They both have many similar connections to other books, so they play similar position in the network -- I could choose either one, and be happy.

Update: Thanks to Laura C. Tisdel, Editor of Too Big to Fail for sending a copy of the book after reading the above!

Visualizing Twitter Lists

The network map above shows the links between people on the Twitter List: Network Analysis, maintained by @valdiskrebs. Two individuals are connected on the map if they both follow each other on Twitter.

The size of the node corresponds to how "integrated" the person is in the following relationships amongst Network Analysts on the list. This network metric -- Integration -- goes beyond geodesics, and looks at paths of varying lengths in the network. The larger the node the more it is "in the thick of things" of Twitter conversations about Network Analysis.

We assume information and knowledge arrive both directly and indirectly on Twitter, based on who you are paying attention to, and who they are paying attention to. The timing of a Tweet can be very important depending who picks it up and ReTweets it to their Followers. Therefore, it is good that a Tweet/idea follows several paths and arrives at different times.

An interactive version of this social graph can be found on the orgnet.com site.

We thank @marc_smith for the Twitter Following data provided in March 2011.

Mapping Twitter #Chats

This is a network map of the almost 1000 tweets during the #ideachat 1 hour session in November 2010. Individual participants in the chat are shown as purple nodes and the "whole group" is shown as the large green circular node. If someone tweeted to everyone in the group, at least twice in the session, an arrow would be drawn from their node to the big green node. People who tweeted to each other [@ messages or RTs], at least twice in the 1 hour session, will have arrows drawn from the tweeter node to the subject node. @blogbrevity <--> @cocreatr indicates that they both sent 2 or more tweets to each other during the session. [We do not show the hundreds of single tweets in the session -- we are looking for key participants.]

Node size on the network map reflects a new network metric we are experimenting with called "attention" which tries to determine both quantity and quality of links pointing at someone. It's not just the number of tweets pointed at you, but who they come from that matters. We will also post an interactive version of this map that will allow you to filter on the type of tweets and their timing during the 1 hour session.

From: blogbrevity's posterous. Thanks, Angela!

Holiday Connections

Merry Christmas! Priecīgus Ziemassvētkus!

Remember, the holidays are great times to re-energize, re-activate and re-weave your networks! During the holidays we come in contact with people we do not see the rest of the year. They bring us new information, insights and intersections from the different networks they play in. Connect, communicate and celebrate! Let the merriment flow and the overlaps emerge!

Happy New Year! Laimīgu Jauno Gadu!


Original Holiday Network Art - © 2008, Silvija Krebs

Networks on the Radio

I recently had the opportunity to be interviewed by Nora Young of CBC Radio in Toronto for her wonderful program: Spark. She is a great interviewer -- puts the subject at ease and asks very interesting questions. The interview covers basic aspects of social network analysis, including privacy issues with the data. An MP3 of the interview is available as well as this more information on this web page.

The day before the mid-term election in the USA, November 1st, I will be on the Brian Lehrer Show on WNYC discussing political networks. The network map below shows some of the political ties of the two gubernatorial candidates in New York.


It is not surprising that the political networks of people in politics are not that much different in pattern from the network of political books we read. In my previous mapping of networks of political books, the general pattern has remained the same, though the books [nodes in the network] have changed — strong clusters of Red and Blue with a thin strand connecting them.

In the Dance of Debt — who is leading?

This network map shows the key roles played in the recent mortgage meltdown. Starting with Main Street on the left we connect the dots out to Wall Street on the right side of the map. The nodes in the network show the various players in the mortgage meltdown, while the links show the exchange of money and value.

Who is responsible for the meltdown? Some blame the poor — they bought houses they could not afford and lied on their loan applications. Others point the finger at Wall Street investment bankers extracting wealth from the working classes. Additionally there were plenty of "middle men" enhancing the flow between the players at either end of the network. Who was critical to the meltdown?

I approach this question as a network scientist — my initial hunch is to look for interesting patterns and players. The network metrics of Connector and Integrator show us that the Mortgage Lender and the Issuer [Wall Street Bank] are key roles in the above subprime network. Each player appears key to each side of the network. The Lender on the left and the Issuer on the right.

Both Main Street and Wall Street participated in this Dance of Debt.

The mortgage lender sells the sub-prime mortgages they have originated, which get packaged into a — residential mortgage-backed security. With the Rating Agencies providing a positive picture of these securities, Wall Street discovers that they can sell them to investors in various forms including CDOs — collateralized debt obligations. The more Wall Street sells the more they want to sell. The ball goes back to the Lenders who further excite their part of the network to generate more real estate sales with new mortgages for securitization. Then Wall Street excites their part of the network by creating more investment vehicles. Send us more, they demand! And so it goes back and forth at an ever increasing fever pitch. No one wants to stop the game.

Who is to blame? Once the dance starts, no one wants to be left out, everyone steps to the emerging tune. The aggressive mortgage broker on Main Street and the re-mix master on Wall Street are each in their element, but they both hide/ignore information to speed up this dance of doom. Everyone influences those in their immediate network neighborhood. While each player acts rationally for himself — to outsiders, the overall system appears more and more irrational as it builds feverishly towards a bubble that will surely burst.

There is no lone culprit. Maybe the system itself is to blame? It was set up to generate positive feedback loops with no monitor of levels or cutoffs when things get out of hand. Maybe the Rating Agencies should have owned the kill switch? Without overly positive ratings on the CDOs, the Wall Street side of the network may not have worked.

Another generator was the ability to play both sides of an investment [sell it to investors and bet against it on a short] allowed the Wall Street end of the network to remain active. In desperate times during 2006-2007 Wall Street banks even arranged for their CDOs to buy parts of other CDOs creating a massively interconnected and interdependent system that would quickly crash once a key connector failed.

Another problem I see in the subprime mortgage flow map is network risk. Investors, who are dependent on Home Buyers making regular and timely payments, are separated from them in the network. They are beyond each other's network horizon, the investor's have no idea of the paying capability of the home buyers they have invested in. In networks, any path longer than 2 steps is usually considered "over the horizon" -- one cannot see, nor influence, over the horizon. In networks, distance leads to distortion, delay, and increased risk. Whose job was it span this network horizon and provide useful and accurate information across the chasm?

We need some way to map and monitor our complex finance systems before they get out of control. Let them operate as open systems but monitor their levels, exchanges, and outputs. As we now map the flow of aircraft from airport to airport, we should map the flow of large transactions from bank to bank. We need a transparent and trackable process so that we are not surprised at outcomes, and are prepared to take action when certain critical conditions emerge.

Update: We helped CNBC look at the financial flow networks in "Goldman Sachs: Power and Peril"

The Recombination Machine

Information does not want to be free.

It wants to be re-combined!

Above is the best recombination "machine" we have — the network. The fuel it uses is information, which does not get destroyed in the process. In fact you end up with more, and better, fuel than you started with... in the possession of more people!

This blog post is a recombination of me and this.

Back of the Envelope Twitter Metrics

The Pareto principle — 80% of the effects/outputs come from 20% of the causes/inputs — works in a variety of situations. The 80:20 rule is useful because it allows us to make decisions based on rough data and a simple calculation. If we could get 80% of the right answers from just 20% of the inputs we would have a great Back-of-the-Envelope [BoE] metric!

Twitter has been swamped with many new metrics in the last year, most of them trying to figure out who the influential tweeters are. I wonder…

Are these scores significantly better than simple BoE metrics?

How accurate are these scores in predicting actual behavior?

How much sociological rigor do they contain? The math is easy, the sociology is hard.

Let's examine a progression of BoE twitter metrics.

The first quick influence metric was "number of followers." The thinking went, the more followers you have the more influential you are. On the surface this appears too simplistic and it is easily gamed and distorted. For a while many early adopters on Twitter where in a mad rush to get more followers — quantity over quality. Some vendors still make money by selling "followers" to fools. But most people on Twitter understand that having random people, who do not know you, follow you, does neither party any good. Followers are a much better estimate of popularity rather than interpersonal influence — Hollywood stars have many followers.

The next BoE Twitter influence measure that gathered some interest was the ratio of Followers to Following. This measure had some background in the various prestige measures in social network analysis. A person in a network is prestigious if they are sought out by many others, while at the same time they do not seek out many others themselves. We divide the number of Followers by the number of Following. Let's call this the FFR ratio. The higher the FFR number, the higher the prestige. A ratio [FFR number] greater than 5 is starting to show some real prestige.

This ratio is a step forward, but it can also be deceiving. Often when people first join Twitter they start by following many people -- those they recognize and those who are recommended by friends. Newbies on Twitter often have the FFR ratio reversed. They follow 5 people for everyone that follows them — until people get used to having the newbie on-line and start following them back. This ratio is a good indicator of not just real new users of Twitter, but can also indicate spambots. Spambots follow many -- often at random -- but are not followed back by any except those who automatically follow everyone who follows them -- a bad idea.

The FFR can be gamed, by keeping the Following count artificially low, though there are often good reasons for being selective in who you follow and keeping that group from getting too large. FFR also gets in the way of people who are very social on Twitter. Some people follow many, but not all, of their followers so that they can carry on direct conversations [DM] with them — only people that both follow each other[have a symmetric tie] can DM reach other. The Following number for this strategy can get high quickly, resulting in their FFR rarely exceeding 5, even though these very social folks may be influential.

Once Twitter Lists started, many believed that List membership would be what number of Followers never was -- a better indicator of influence, of those we listen to. Lists are much harder to create, and people that make them usually put some thought into who belongs in what category. Lists do make the transition from popularity to influence, but they do not eliminate the problem of pure popularity. We known popular tweeters still end up on more Lists than their less well known brethren. How do we eliminate/reduce this overbearing popularity factor?

We can not eliminate the popularity factor, but we can minimize it by using it as the divisor in a ratio — the greater the popularity, the less affect it has on your Listed score. Dividing the number of Lists one is on, by the number of followers gives us a pretty decent BoE that anyone can calculate -- all of the numbers are under each person's profile. Let's call this the Lists to Follower's Ratio[LFR].


LFR = Lists / Followers

LFR gives us a number less than one, I use the first four digits to the right of the decimal for the LFR score. Using my numbers from the graphic above we calculate an LFR of 1056.

Let's take a look at several people I know on Twitter. We will look at Clay Shirky [@cshirky], Venessa Miemis [@VenessaMiemis] and myself [@valdiskrebs]. Looking at Followers only, Clay wins easily, followed by Venessa, a distant second, and then me. Next we look at the FFR. Here again the number of Followers dominates the calculation and again Clay gets the highest score by a great margin. Clay is a popular author and blogger and is very popular on Twitter also. Many newcomers to Twitter probably automatically follow Clay. Several newbie Twitter apps recommend to follow the popular people on Twitter such as @scobelizer, @timoreilly, @cshirky @techcrunch and others. This just increases the popularity of already popular people. Maybe most newbies don't know why they are following these people, nor know who these people are? They just follow them.

Next we will look at List membership by itself. Here again popularity skews our number. Same results as before -- Clay is first with Venessa and Valdis a distant second and third.

I have talked about hidden and local influencers before. They do not have the reach of an Oprah in recommending books, but they have a smaller, focused, audience that seeks their advice and opinions on one or more topics. How do we find them? I think the LFR BoE metric gives a decent indicator of who is one of these hidden/focused influencers. Running the LFR metric [looks at Lists and reduces for Popularity] we get a totally different result! With popularity diminished, we get a different view into who is looked to advice, ideas, opinions and expertise. Now Venessa is number one with an LFR [as of August 21, 2010] of 1679 [first four numbers after the decimal]. Valdis is second with an LFR of 1056 and Clay is last, for the first time, with an LFR of 0614.

Clay is popular, but Venessa and Valdis are listed as topic experts/advisors/influencers beyond their generic popularity.

Both Venessa and Valdis are more focused on topics they address and they attract a more focused, yet much smaller, audience. People put them on many Lists according to their speciality. People get to see Venessa's and Valdis' opinions and ideas — their following grows more slowly. It is an active choice by their followers — they know why they chose to follow/list either of these two. The decision to follow an already popular person is often a passive choice... "everyone else is doing it, they must know something I don't" goes the thinking. An active decision to listen/follow someone is probably a much stronger basis for the transfer of ideas and influence.

The LFR is also a good metric in uncovering spammers or tweeters pushing an agenda — whether it is ads or only their own content. The difference between Followers and Lists will result in a very low LFR, with two or more leading zeroes.

A very quick way to gauge your LFR is to drop the last digit from your followers [i.e. 1234 followers becomes 123] and compare that Follower proxy your Listed count [i.e. 321]. Is the new Follower proxy LESS THAN your Listed count? If so, you are probably more influential, than popular. Anyone can do this calculation while staring at a person's Twitter Home page and deciding whether to follow, or not.

Another quick way is to calculate a "batting average" like in American baseball. Take two people, Rita and Ralph, they each appear on 200 lists. Rita has 1000 followers while Ralph has 4000. Rita's batting average is 200/1000 or .200, while Ralph's is 200/4000 or .050. Rita get's a "hit" more often than Ralph.

There is a new Twitter metric that looks promising — PeerIndex. I know the person behind it, Azeem Azhar. He has been thinking about social networks since the mid 1990s. He and I had a wonderful discussion on this issue of Release 1.0 [large PDF] that I wrote about social networks in, and between, organizations in 1996. I like the way that PeerIndex divides one's influence by topic. Look at the "key authorities" in social media on PeerIndex — they are not the usual suspects that other Twitter metrics seem to echo. Azeem understands that in addition to the technology, he has to get the sociology right!

BTW, almost all of those listed as highly influential by PeerIndex for the topic "social media" [as of 8/21/2010] also score high in the BoE metric LFR — 4/5 or 80%, just like the Pareto Principal predicted!

Update: Here is another good use of lists! Create a Twitter List of those who have listed you and check that regularly for serendipitous opportunities! Hat tip to social media guru @arturs in Latvia.

Can you hide in the networked world?

So, I am clicking through the PC World web site and I spot an article titled:

"Google CEO: Change Your Name to Escape Our Watchful Eye"

As a network thinker, I stopped dead in my tracks -- that is a very stupid statement by a very smart man. I wondered, "Why is he saying this?" Surely, he knows his company contains many scientists that can find anyone in the net, no matter what they change their name to. Is he just blowing smoke in the interviewer's eyes?

Schmidt goes on to make some very interesting statements in the interview...

"'I don't believe society understands what happens when everything is available, knowable and recorded by everyone all the time"

I totally agree with that, and have been sharing a similar warning for a while.

I read on and saw that Schmidt was talking about changing one's name at one particular point in time -- the transition between youth/college and adulthood/career. Now his statement makes more sense, but it is still very hard to do.

So, all of you network thinkers out there reading this... why is Eric Schmidt most likely wrong? Even if changing your name was as easy as changing your address, why could you not hide from your past on Google, or from any of the dozens of internet data mining firms/governments out there?

Fill up the comments below with your theories and ideas!

Connecting the Dots in the Mortgage Meltdown

I wonder if a Goldman Sachs executive, or investor, would be willing spend a night in the house above? After all, they do kind of "own" it.

 The house, in the Slavic Village neighborhood of Cleveland, had a mortgage that was part of the Goldman Sachs synthetic CDO, ABACUS 2007-AC1. It was this synthetic CDO that the US S.E.C. took Goldman Sachs to court on. This home was also one of the properties that mortgage broker Mark Kellogg was convicted on [link to video].

In 2008, the house was foreclosed upon, and still sits abandoned today. At least the ivy devouring the south side of the building seems happy.  For more details, diagrams, and how we connect the dots between Goldman Sachs and this gold-less neighborhood, see our orgnet.com web site.

We have been "connecting the dots" since 2001. First, was the terrorist network of 9-11 hijackers. A very interesting and innovative project was done by one of our clients -- uncloaking a slumlord conspiracy. Finally, we can share a few other examples: corruption in local government, political influence via indirect quid-pro-quo and financial flows.

All your social graphs are belong to us! [sic]

I got off Facebook over 2 years ago. I am smiling as others publicly proclaim their freedom from, or their disappoint with, the (still growing) superorganism.

Facebook is currently facing a barrage of negative feedback about its privacy policies and methods. Yes, these are valid criticisms, but they are not Facebook's achilles heel. They have a bigger problem. It is the structure of Facebook that foretells it a fate of AOL -- a popular online site in the 1990s that grew quickly, with great promise and PR, and is now a much smaller collection of speciality sites. Back then AOL was also supposed to be the "new internet", just as some are predicting now that Facebook will be the new WWW.

Facebook, and all other online social networking sites are structured wrong. They are places where we have to go to connect and communicate. That is not how we naturally connect and interact as humans! Their technology does not support our natural and inherent sociology. Yes, we meet people in places and on sites, but once established the relationship does not remain only there.

Facebook is like a land line.

The telephone land line and the old Ma Bell is good metaphor for the future of Facebook. With a land line you had to be "in a place" to receive a call -- at home, or at your desk at work. If you were not there you missed the contact and you probably did not know you missed it until you returned to the place. Messages, voice mail, beepers and other technology tried to fix the problem, but it persisted. Ma Bell owned all of the switchboards and had centralized control/ownership of the network. Facebook also has centralized control/ownership of the data base of social connections/objects -- the social graph. One source shopping -- take it or leave it. All your social graphs are belong to us!

Our personal networks, as a whole, are not centralized in once place, they are distributed as we are, wherever we are. Mobile technology allows the call, the connection, to go where we are -- the device is always with us. We can make an immediate decision if, and how, we will take the connection. If we decide not to, we know right away that an attempt has been made and from whom. This is how we naturally network -- we decide on the fly, who to talk to, in what voice, and how much to share. I may deal you differently tomorrow than today depending upon the current context. Mobile technology also breaks up the centralized data base of connections and objects and allows for a distributed model. We each have our data, but we share a common protocol [PTTP? = person to person transfer protocol].

I am talking about a NON site-based social network!

Putting Facebook on mobile devices does not solve the problem -- it is like adding "speed dial" or "call forwarding" to your home phone. Facebook does not allow for natural flexibility of human interaction, you and your relationships are ossified in their computer code and in their data base. In a truly networked world we do not have to go anywhere to connect to others -- we just ping from where we are at, and wait for the response from where they are at -- peer to peer without going through Queen Between (Ma Bell/Facebook).

Facebook is still as popular as sliced bread, but many people are seeing it in a new, unflattering way. They have stopped blindly trusting Facebook. The digerati will leave first, and then the later adopters -- leaving the AOL crowd behind.

Update: So, what I am suggesting is space instead of place. In space you have no definite location/place, you only positioned in relation to everyone else in the space. You are located by who you are connected to, it's all relative. Quantum Physics explains this with atomic particles. We need to understand this with social entities also.

Update2: Another good analogy for current social networks are the email systems of the early internet — they could not talk to each other. You had to join each system to be able to receive/send information with everyone you knew. Once SMTP came into play the walls between the systems fell down. You could now have one email identity and connect with any other identity -- regardless of place/membership. What is the SMTP for online social networks? Webfinger may be a promising technology for un-siloing our on-line social networks.

Subprime Mortgage Flows into Goldman Sachs

On April 16, 2010 the U.S. Securities & Exchange Commission filed a lawsuit against Goldman Sachs alleging securities fraud for selling residential mortgage backed securities [RMBS] the firm knew was made up of failed sub-prime mortgages.

Using our InFlow social network analysis software we linked the failed/foreclosed mortgages from Cleveland, Ohio that ended up in investment vehicles sold by Goldman Sachs. The failed investments were made up of mortgages from all over the country. The map below shows only those failed mortgages originating from Northeast Ohio.

The outer ring on the map [black nodes] are actual properties in Cleveland and NE Ohio with failed mortgages. The next ring, of blue nodes, are the various Trusts that mortgage-lending institutions created to securitize the mortgages and sell to Wall Street. The inner ring of green nodes are major banks that created and/or administered the Trusts and finally the focal point of all inflows is the magenta colored node -- Goldman Sachs.


The customers who bought these financial instruments had not way to fully evaluate the basis [sub-prime mortgages] of what they were investing in. They trusted the intermediary, Goldman Sachs, who they did have a direct relationship with. In networks, distance supports deception and distortion. Network distance also gives Goldman Sachs plausible deniability.

Each major municipality in the U.S. probably has a map of failed mortgages flowing to Wall Street. Goldman Sachs was not the only investment bank selling such products. We have a map for each major investment bank, showing which failed Cleveland mortgages they packaged, and which intermediaries they worked with.

Beautiful Visualization

April 15th is tax day in the USA, and also the publishing date of "Beautiful Visualization" by the O'Ŗeilly empire! I have a chapter in this book on network visualization. The book will be published in full color.

In my chapter, I discuss how to derive network maps from simple data like "the choices people make" and "the events people attend." I apply social network analysis to easily accessible data. There is a lot of relational information in the public datasphere... I show you how to spot those relations, and look for interesting patterns.

Overlapping Networks

We often think of our networks as belonging to us, or our group/team/family. We imagine they have an identifiable beginning and end. We want to draw borders to define "yours" and "mine." Yet, in reality we cannot. We really cannot define where my network stops and yours starts... no matter if you are a person, group, organization, or country. We are all intersected and our connections overlap with those of our network neighbors. Boundaries are fuzzy, at best.

Let's look at a simple example. Organizations, whether for-profit, or not-for-profit, usually have a Board of Directors. We can think of this Board as a network that belongs to the organization. All members are linked if they sit on an organization's board together. We might view the Boards of the top 50 U.S. companies like the diagram below -- individual clusters, each belonging to the parent company. The gray links show co-membership ties between the individuals.


Directors are not limited to the number of Boards they can be members of. Board members are limited to the number of Boards they sit on only by time, energy and invitation. Below is an example of a Board member who sits on the Board of two companies. This may be Steve Jobs, who sits on the Board of Apple and Disney.


We now choose a different color for those Directors who sit on multiple Boards. We see how the Boards of the top US companies are actually interconnected in the diagram below. Blue nodes are Directors who sit on multiple Boards.


The blue nodes in the network above are conduits that move information, ideas, and knowledge between the clusters -- they are the intersection where two networks overlap. Contagion between corporations is often based on flows via Boards of Directors. We apply social network analysis [SNA] to this social graph and we see who may be key in this diffusion process. We apply a new SNA metric, I call Awareness [measures potential awareness of a node to what is happening around it (directly and indirectly) based on it's pattern of connectivity]. Those nodes with higher awareness are shown in a larger size in the diagram below.


It is usually beneficial to be connected to those who have a good view of what is going on. Information and knowledge is often shared [intentionally or unintentionally] with trusted others, close by. Information leaks and flows, but never too far. Board members who are connected to other highly-aware Board members, have a higher probability of finding out more -- but the range is limited. Even those who just sit on a single Board can increase advantage by being connected to multiple blue boundary spanners. This is reflected in the diagram below. Node size is derived from awareness of what is happening in the network. Some Boards have greater awareness of what is happening in the corporate world.


This was a simple illustration. The actual network between the Board members will be denser, based on their possible multiple ties -- employment, memberships, and other current and past associations. The full multiplexity of the individuals was not known, nor shown. Yet, we see how even some knowledge of a social system increases our potential to target messages to influence that system. Of course, the better our data, the better our targeting. A telescope may be preferred, but even binoculars provide advantage over the naked eye. And binoculars that reveal what is usually invisible, are even more useful!

What complex social systems do you want to look at, and interact with? What overlaps can you utilize?

Update...
My colleague, Balazs Vedres, calls these intersections between overlapping groups "structural folds". A person who spans a structural hole [Burt] connects two groups but is a member of neither. A person who connects two groups via a structural fold is a member of both! The social dynamics of connecting groups these two ways are quite different.

Spread of Influence in a Network

About a week ago I put up a simple quiz on TwitPic using David Krackhardt's kite network as focal point. The kite network above shows a small group of people with strong symmetric[two-way] communication links. I asked,

"Where would you plant your msg in this net? Why?"

Several of my Twitter followers immediately answered and then the post was re-tweeted by several friends of mine. One friend sent it out to 30+ "social media mavens" -- none of them braved an answer.

This is a toy problem, yet it helps us think about how information and influence spreads in a human network. When I present this problem during one of my many talks on this subject, the first answer from many voices in the audience is usually "Diane." Then there is a period of silence and a few people sheepishly offer "Heather." Finally some joker in the back of the room yells out "Jane" and everyone has a good laugh.

So, what is the right answer? There are several.

The most popular answer of "Diane" is not a bad answer. The eye is attracted to the hub structure around her. She has the most connections and does reach a majority of the network with a direct connection.

The choice of "Heather" is a good one -- my preference. While Heather has only three direct ties, she reaches everyone in the network within two steps. Diane has several longer paths to reach everyone. Information and influence both degrade with each step in a network. After one step the message begins to grow fuzzy, after two it is becoming very noisy, and after three it is basically useless -- background hum. We might be all separated by six degrees but it is the first two steps that really matter.

Another good answer is "Fernando or Garth". They are between Diane and Heather and can also reach many people in the network quickly. Those that know social network analysis come up with this answer because these two guys have the best closeness centrality.

All of the good choices mentioned above are not guaranteed to get your message passed around even this small network. Just like a forest fire depends on one burning tree igniting another tree, or two, Diane, Heather, Fernando, and Garth all depend upon others to continue passing the message. It is not just the seeded node that matters, but the network neighborhood that the seed is embedded in! And... each node has a different threshold of adoption -- for one topic Carol may be a slow adopter, while Ed may be quick, and vice versa for a different topic/idea.

I am reminded of this song by the Alan Parsons Project -- The Turn of a Friendly Card:

"The game never ends, when your whole world depends,
on the turn of a friendly card"

Or in today's world -- the turn of a friendly tweet!

We have a simple problem, with no simple answer. So, how do you work this?

What happens when we try to scale this to real human networks that have dozens or hundreds of interconnected friends or colleagues in a network like below?


The secret is... redundancy! Yes, redundancy, that concept that we tried to eliminate in the 1990s with untold hours and dollars of business process re-engineering. Some redundancy actually helps networks function better.

In the simple kite network above we would use redundancy to seed the message with Diane and Heather! Some people may not hear Diane today, but will pay attention to Heather tomorrow.

In the real human network above we might need to find a dozen or more places to plant our viral visitor. Social network analysis software can help us discover the best soil for planting!

Update...
So, I tweeted the link to this blog post when I finished writing it: evening Eastern Standard Time in USA [GMT -5]. The response was not great. bit.ly showed me that 25 people clicked on the link in the first hour. The next morning I tweeted the link again and now more of my Twitter followers were paying attention -- by Noon bit.ly reported over 300 clicks. Finally I ran another tweet @ midnight, for all of Pacific folks, and early-risers in EU and RU. This added another 100 clicks accroding to bit.ly.

Lesson learned: It is not only the persons you plant the message with, but also the timing of the message. People pay attention at different times -- especially on Twitter. Redundancy in timing works too.

New Window on Social Network Analysis in Organizations

What do you see when you look out the window above -- the structures or the sky?

The Gartner Group is a well-known, worldwide advisor on information technology issues. They have been a fan of social network analysis for several years now. Recently, they sent out this press release about how social and organizational network analysis fits into their concept of "Pattern-Based Strategy." Gartner describes their approach as focusing on "business patterns to capitalize on opportunities or avoid disruptions." Gartner is expanding their view -- they are not just talking about Windows® any more.

My partners and I have been analyzing networks in, and between, organizations and communities for over 20 years, and we certainly can attest that what Gartner suggests is true -- the key is in the connections! We have repeatedly found patterns in adaptive and agile organizations that are not present in companies that struggle with similar opportunities and disruptions. We are delighted to see an influential firm like Gartner now looking in this direction and seeing what we see.

One of the interesting things about organizational patterns is that they do not follow a strict recipe or design. However, good network patterns do provide highly similar benefits -- the ability of the human system to learn from and respond quickly to both opportunities and disruptions. The structures [both formal and informal] in Company X may not be the same as the structures in Company Y, but each may be optimizing a pattern, uniquely applied to their situation. We focus on the similar patterns we see across diverse organizations. What do they have in common? These organizations are succeeding because of their ability to exchange information/knowledge, learn quickly, and become aware of their environment -- irrespective of their particular hierarchies or business process designs. It is the pattern of the emergent organization –- what happens in the white space on the organizational chart -– that leads to adaptability and agility, and ultimately to success.

We can show you where your organization or community is on that scale of adaptability/agility and help you adjust your patterns for increased success with opportunities and disruptions.

Lets open up a new window in your organization...

What do you see when you look at your organization -- the organization chart or what goes on behind it?

Picture above is from my favorite young photographer -- Alice Merkel. See more of her portfolio on Flickr.

Your Presence after the Presents

After seeing this cartoon by Ed Hall, I started to think about personal networks. What will your personal network activity be like this holiday season?

What will your presence be, after the presents are opened?

Here or There?

Let's look in on a typical family gathered for their holiday celebration. Mom, dad, kids, and grandparents. Where will the conversations be? In the room or outside the room? Local or Global?

With digital tech on the wish lists of all age groups, will each withdraw into our own world, focused on their new device... even while they sit within arms length of their close ones? Or will the conversations span local and global, with everyone in the room sharing what they are seeing/hearing out on the Net? Will the local/global perspective change as the family sits down to their holiday meal? Or will that red-blinking Blackberry be right next to the wine glass?

Will your conversations be with others in the room? In the social network analysis map below the family members all gathered in the same space. Dark red links show who is talking to whom F2F via voice.

Or will your family look like the cartoon above? In the same room, but not necessarily with each other? Each off in their own world? Grey nodes are friends and acquaintances accessible via social media. Blue links show who is interacting with whom via text.


Maybe the outside can be connected to the inside... diversifying the conversation? Interesting items from the periphery are brought into the core conversation.


Think about the digital technology in your family this holiday season... where does it enrich and where does it exclude? How do you get it to include and invigorate?

White House Visitors

Recently, the White House revealed who has visited with members of the Obama Administration and when and how long that occurred.

Above is a network map of the who visited with whom in the White House: Visitor --> Visited. The larger the node, the more visitors received. POTUS is short for President of the United States. The links show only the smaller meetings of less than 1 dozen people in attendance. Our assumption is that you have to be more important to be invited to a small, focused meeting than to a large general gathering. Small meetings tend to reveal personal or business relationships. As we now know, even fakesters can sneak into large social affairs.

There is more data with more connections to be mapped in the White House data, but this map shows what is going on in the thick of things.

Too bad previous administrations did not have the cojones to provide the voting public a peek into the workings of government. Glad to see data transparency on the march!

A more detailed view of all of the players...

Network Weaving 101

The basic skill in network weaving is the "closing of triangles".

A triangle exists between three people in a social network. An "open triangle" exists where one person knows two other people who are not yet connected to each other -- X knows Y and X knows Z, but Y and Z do not know each other. A network weaver (X) may see an opportunity or possibility from making a connection between two currently unconnected people (Y and Z). A "closed triangle" exists when all three people know each other: X-Y, X-Z, Y-Z.

Let's look at a real life example of network weaving.


Here we see our friend and colleague Ed Morrison, of iOpen, connected to two of his clients -- the economic development folks in both Lexington, KY and Oklahoma City, OK. He knows each of these groups, but they do not know each other. Much could be learned if both of these groups shared their economic development experiences with each other -- innovation happens at the intersections.

But you can't introduce groups to groups, or organizations to organizations -- it works better by introducing people to people. So, Ed picked two leaders from each group to close the triangle. He picked Cynthia Reid at the Oklahoma City Chamber of Commerce[OKC] and Lynda Brabowski of Commerce Lexington[CLX]. This triangle is illustrated below.


When Lynda expressed a desire to Ed for CLX to visit another region that they could learn from, Ed immediately knew the answer -- visit OKC, who previously had faced similar issues and handled them very well. Ed, also knew which introduction to make -- a network weaver needs to know WHOM to connect by knowing the people, the groups, and the dynamics involved in the connections that are being made. The closed triangle -- after Ed's introduction -- is shown below.


This was not the end of this weaving opportunity. Ed accompanied the CLX folks on their visit with OKC. During the trip he closed a few more triangles. Ed introduced the CLX group to two of the key architects of the economic blossoming in Oklahoma City, Ron Norrick -- the former mayor that started the effort, and Burns Hargis a key OKC board member. Those closed triangles are below.


The cool thing about closing triangles is that anyone can do it, and you do not need anyone's permission to do it! Close triangles around you wherever and whenever you see an opportunity!!! You and your community will benefit.

After you close key existing triangles, open new ones for potential closure later. It is just like gardening -- once you gather the current harvest, you need to make plans for future harvests. Open new triangles by meeting people not in your network neighborhood -- diverse sources of knowledge, opinion and experience. Like any garden, a network also needs pruning. Don't feel bad about pulling "weeds" in your garden, or not replacing certain plants for the future -- just be careful you don't damage the roots/links of the rest of the garden!

Originally posted by Valdis Krebs in Network Weaving June 23, 2006.

You may find yourself... living in a large network, and you may ask yourself... well, how did I get here?

Inspired by my favorite Talking Heads song: "Once in a Lifetime".

We often wonder "how did I get here?" when we look around and reflect on our personal networks. Where did all these connections come from? Did I do all this? Who helped weave my network? What can I do with these connections? Where can I add more?

I will go through key growth stages of a network that evolved this past decade. Many of the connections have already resulted in creative collaborations. Other connections are just bearing fruit now. Networks are like that -- a new connection does not always bear instant fruit, sometimes the growing season for some links is very long. Yet at the end, the fact that the link is already established, an opportunity is spotted and acted upon using the resources that the link provides.

Many years ago the network looked like this. Two people are connected if they interact with each other as friends and/or colleagues. ONet represents a now defunct on-line group: The Omidyar Network. This was a gathering place to help people discover how they can make a difference.


People on ONet got to know each other from their on-line activity and Jerry introduced Tom to Valdis -- he closed the triangle amongst himself, Tom and Valdis.


Next, Tom introduced Jean to Valdis at a seminar he organized in Boston. Soon after that, Steve reached out to June after doing a web search on "network weaving."


In the next phase, June introduced Steve to Valdis to work on network mapping, and Valdis introduced June to Tom to speak at his next seminar in Europe. Notice as people start "closing triangles" via introductions, the original meeting place for a portion of the group -- ONet -- starts getting pushed to the periphery.


Next, Valdis introduces June to Jean to share similar interests and goals, and after working with Valdis on network maps Steve introduces Daniel, who is also interested in network mapping, to Valdis.


Finally, Jean meets Jerry at another event and the network as it stands today is now in place.


When we make introductions, and close triangles, we are not doing it to merely create new connections. Network weavers usually have a goal in mind when connecting two new people -- a project, a mentorship, a future collaboration. The links between Daniel, Jean, and Valdis were in place several years ago but only this year did they all collaborate around a common project. Jean and Valdis were working on thrivable networks and Daniel was organizing a conference around building networks to help inner-city kids -- all three were going to be in Chicago the same week. After a few emails it was agreed, Jean and Valdis would do a workshop on building thrivable networks @ Daniel's Tutor/Mentor Conference.

So, networks are built in many ways. First, by being in the same physical or virtual space, and second by active network weavers who make strategic introductions for the benefit of those they connect and for the benefit of the entire network. Networks are also activated in many ways. Sometimes by the initial introduction and connection to an immediate need, and other times, existing links need a little nudge to activate -- like an obvious opportunity. Our themes in the workshop will be:

• Know the Net - map the existing connections of your community/ecosystem
• Knit the Net - weave and support new connections, build a thriving network
• Nudge the Net - activate the network toward self-organization and action

Register here online and join us in Chicago on November 20th!

How did we get here?
Letting the days go by...
Many years of knowing, knitting and nudging.
Same as it ever was... Same as it ever was...