This first map provides multiple connections of Haas alumni to extant companies. Companies appear if there are two or more Haas alumni as board/management team members in the LinkSV database.

Example 1

Here, we see that two companies have three or more Haas affiliations: Tube Mogul has three management team members from Haas and Delivery Agent has one management team member and two board members from Haas. Exploring further, below is a map of Haas alumni that currently have the title CEO (registrants goto Person Filters Title=CEO and Apply Changes).

Example 2

Here, we see a list of 12 people who are currently CEOs at companies. This could be expanded further by clicking “add to map” for any of the 12 people. Roost CEO Alex Chang was added to the map and then we removed Haas as a target node. You can double-click on any person to pop-up their LinkSV bio.

Example 3

Here, we see Alex Chang’s multiple ties to Juliya Rubin (TotalMove, Roost) and Myles Felsing (Haas, Middlebury).

The next examples are two Cluster Maps, which effectively removes Haas as a target node and highlights other pre-existing relationships amongst its alumni (MinLink=3) map which only depicts triads or greater clusters.

Example 4

Here, note the strong relationship to Accenture (n=8). Note again ties to TubeMogul and Delivery Agent.

The next map is the Haas Cluster Map with School Nodes Collapsed into Links (registrants goto Information window Controls).

Example 5

Here, note two large clusters in the map.

We can specify these clusters from the information window Controls by unchecking Collapse School and checking Collapse Companies, Investors, and Experience, leading us to…

Example 6

Here we see the aforementioned Haas clusters as other university ties, the strongest tie being UC Berkeley (+non-MBA degree, n=32), followed by Stanford (n=9), Cornell (n=8), UC-Davis (n=5). This map can be used to identify potential Haas groups based on common education affiliation. Note how double Berkeley Alumni can be identified as strong School Ties toward potential collaborative efforts. It is surprising to note the high number of ties to Cornell (my old stomping grounds) and the surprising Upstate NY-Bay Area connection.

We hope you can use some of the new features of LinkSViewer 1.3 to your own strategic advantage. Feel free to sign up for a free two-week membership by clicking on Free Trial Registration.

First let’s look at the networks of the three key board members identified in the previous post: Reid Hoffman, David Sze, and Bill Gurley. Q: What do these board members have in common? The goal here is to identify companies not found in the previous post map - companies that are found in the same circles as the ‘hot companies’ discussed in the last post. Along with the new people we find, all of these companies are potentially interesting, so let’s see what we can dig up.

In the last post we started with a map of company nodes. In this first derivative map we will look at the network formed by the affiliations of those companies - namely, the key board members.

In the map above we see the combined network for our three key board members: Bill Gurley, David Sze, and Reid Hoffman. Some key features to note are the central position of Reid Hoffman as an investor which mirrors his importance as a board member in this network formed around the ‘hottest companies.’ Most of the companies from the previous post appear as well (as is expected). Some new names also appear - Marc Andreessen, Ron Conway, Peter Thiel, and Gil Penchina as angel investors; John Dean, Jed Smith, Keith Rabois, and Brett Bullington as board members, and PayPal as a spawning ground for all of this activity. Some of the new companies we are exposed to include: Softcoin, Marquee, Wink, Vendio Services, Six Apart, Open Table, and Tropos Networks. The next map shows the network for the key angel investors from the map in the previous post.

The second derivative (well, kind of). In the map above we’ve mapped the four key individual investors from the previous post. Just as the first map in this post shows a derivative network of the key board members, this map shows the derivative network of the key angel investors. Looking at this map we see that for our network of the ‘hottest companies’ Bill Gurley and Mike Maples are less integrated. Mike Maples isn’t seen in the board member based map above, and Bill Gurley isn’t seen in the investor based map above, but all of the other key players are present in both maps, as well as Peter Thiel, Keith Rabois, and Marc Andreessen. The other point of interest lies in the new companies introduced, especially those with multiple connections such as the Facebook-Powerset cluster between Peter Thiel and Reid Hoffman. Their common involvement in Facebook negates the interest in Friendster to a great degree, but Powerset is an interesting new inclusion. Similarly, Xoom, Revision3, and Wikia are new companies introduced on this map. The next map shows the network for the key VCs from the map in the previous post.

 

The third derivative (kind of). In the map above we see the co-investments of the four key VC firms identified in the previous post. Along with the ‘hot companies,’ and other companies of interest that we identified from the second map in this post, here we see more companies of interest due to the co-investment relations between our key VCs. It is interesting to note that none of these companies have received funding from more than two of these VCs, which perhaps would have been an indication of a higher level interest or importance in the network of ‘hot companies.’ From this point we could continue to investigate the networks of these other companies - Registered users can do so by clicking through on our maps.There is a tendency in the Valley to fixate on the hottest new thing, and the biggest payoff, but the derivatives of ‘the big ones’ can often lead to a number of extremely successful companies. In any case, the goal of this post was to illustrate alternative viewpoints using network maps (i.e., from the VC, Angel Investor, or Board Member perspective). In this case, derivative networks were used to identify hot company relations. The main premise is that it is not only what you do, but who you are associated with, that helps determine company success. This can be applied to any set of people, companies, or investors in your own area of interest.

Final Game: Florida over Washington State in a squeaker. 

Final Four: Florida, Washington State, Ohio State, Pittsburgh

Teams that could go further than expected: Oregon, Louisville, Virginia, Boston College

Teams that could leave sooner than expected: Kansas, Memphis, Texas

Potential First Round Upsets: Long Beach State over Tennessee, Georgia Tech over UNLV

So how did I come up with these predictions?  With network analysis, of course (what else do I do in my spare time?).  Now, it is common knowledge that who-plays-whom is often used in determining sports rankings (e.g., a strength of schedule indicator).  My main premise is that given relations between a finite set of events, network analysis provides a powerful strength of schedule indicator.  This is a new way to model prediction markets and the main implication for this blog is that such a model can be applied to financial markets as well (using relational capital as the basis). 

Granted, every model needs a theoretical basis and for this I turn to the research literature on Small World networks.  Small Worlds have gained in recent popularity based on the original sociological research of Stanley Milgram and modeled in the physics realm, most notably by Duncan Watts and Steven Strogatz.  They argue that the small world phenomenon occurs in two network conditions: (1) high local clustering; and (2) small global path lengths.  Small world networks are visualized as pockets of nodes grouped together with relatively few links required to reach all others (on average).  My goal was to statistically model small world networks. 

Back to basketball…If we apply Watts and Strogatz’ finding to March Madness, this means that those teams that have relatively high local clustering and short global path lengths are best prepared for a round-robin (small world) tournament.  This implies that teams that form relatively isolated clusters with a few strategic links to well-connected others have a distinct advantage. 

Let’s look at a few examples in my NCAA 2007 network graph.  Gonzaga has played a number of tournament teams, but they form no distinct local clusters.  The PacTen has a small world network advantage because of a local cluster with strategically positioned ties to key gatekeeping teams (they are tied together and connected to, but apart from, the central core).  This is particularly advantageous for Washington State and Oregon, who both played Gonzaga.  Long Beach State has played both USC and UCLA (high clustering as connected to the PacTen), but have a longer path length because these are their only two connections in the field. 

In my model, I also factored in some other stuff.  You can check out my resulting bracket here.  We’ll see what happens (and good luck to all). 

Kinda cool.  From a network analytic perspective, the above map highlights PodShow as a liaison tie between the left and right cliques in the above Google Company Map - it connects two clusters without being a part of either cluster.  Plaxo, eLance, and Zazzle are bridges in the network because they connect the left and right cliques together, but are a part of one of the cliques. [Note: Bonus Question for someone who wants to post a comment - we removed the registration requirement: What ties the left, center, and right cliques together?]

Intrigued, I then clicked to the Relational Map of Google, Dropped Pendants, and began increasing the MinLink (+) to isolate strong ties.  I stopped at MinLink = 4 and this result is the below Map O’ the Day:

Here we see the strong-tie capital relations of Google.  Note the ties to PodShow: (1) Sequoia Capital as investor; (2) Kleiner Perkins as investor; (3) Sherpalo Ventures as investor; and (4) Ram Shriram (Sherpalo Ventures) as board member.  In fact, PodShow and Google are the only companies to combine the power investor triumvirate of Sequoia, Kleiner, and Sherpalo (Sherpalo being a fairly new VC Firm).  The PodShow common investor and board ties thus make it the “closest thing to Google” from a relational capital perspective. 

One of the interesting aspects of LinkSViewer is the ability to uncover relationships that may not be obvious to the end user.  In this case, one may be aware that Google had Sequoia, Kleiner, and Sherpalo as investors.  They may even be aware that PodShow has the same three investors.  But the key connection is the ability to see that both have the same connections.  This is the power of relational capital.  Now this may not be much of an “aha” - it is the typical case where insiders will tell you that they knew this after you show them the network.  But more complex relations that are less obvious to the end user.  For example, consider the case of large VC firms connected by their portfolio companies as filtered by rounds, $ invested, or industry sector.  Here, the capital relations can help tell more complex relational stories based on level of filtering (this is not as obvious).  We all have a relational network understanding of the world - some are better at it than others - our goal is to help clarify these connections and also allow users to discover new connections.     

(1) Search for the company in LinkSViewer 1.2; (2) Show the relational map; (3) Company filter out (i.e., deselect) private and acquired companies (registered users only); (4) Drop pendants

The result is the relational map for the target company’s public relational network.  Questions to ask: How have related public companies fared at IPO?  Are they successful?  How much experience do the investors and people have in taking companies public?  Let’s now preview the public relational maps for Riverbed and Google.

Riverbed has relations with two public companies: Polycom (Stan Meresman and Accel Partners link) and eHealthInsurance.com (Christopher Schaepe of Lightspeed Ventures).  Polycom has achieved great success (700% increase in stock price over the past 10 years) over the long haul and the jury is still out on eHealthInsurance.com (4% decrease in stock price since its offering three month ago).  The important part here is that the affiliation between Stan Meresman and Accel seems a winning formula.  Check out the below for a comparison of each stock’s historical prices.  

Riverbed Public Company Performance (Yahoo Finance)

Now we turn to the Google public company relational map:

Google has a more complex public company network, than Riverbed.  This is mainly because of the presence of Sequoia Capital and Kleiner Perkins as investors, but also bodes well because of the volume of experience between Sequoia and Kleiner in IPO investments.  Note also the presence of ”heavy hitter” board interlocks (John Hennessy, Al Gore, Arthur Levinson, John Doerr, and Michael Moritz).  Perusing the public companies connected indicates success (Apple, Intuit, Symantec, Cisco, Flextronics, Electronic Arts all at over 500% increase since IPO), but also some relatively unsuccessful IPOs (NeoMagic, Onyx Phamaceuticals, Redback Networks, and Saba).  The below links comparing the successful and unsuccessful IPOs provide some insight in the historical price trends, however.  In general, the successful companies were successful at onset, especially after one year, and steadily increased in price thereafter; the unsuccessful companies were unsuccessful at onset (save Redback), especially after one year. Check out the below links for a comparison of positive and negative performance comparing each stock’s historical prices.  

Google Public Company + Performance (Yahoo Finance)

Google Related Company - Performance (Yahoo Finance)

In summary, the mapping of public companies provides key information that can be used by investors.  By incorporating historical prices among related companies, the investor has a way to gauge the success of connectivity based on actual price indicators.  The goal is to find network connections, identify a positive public trend, and use this information to guide investing.  Given the two cases of Riverbed and Google, there is evidence that success breeds success, although one must incorporate historical trends to further solidify this conclusion.  Certainly, this is a starting point for further investigation and registered LinkSViewer users may want to explore other filters to aid in their IPO analysis.  Pragmatically, I would keep an eye on eHealthInsurance.com (new IPO) and Saba (10% increase over the past year) to see if they exhibit further increases in stock price.  A couple of caveats: (1) dates of IPO may affect trend comparisons because of market volatility at the time of IPO; (2) given the relative paucity in recent IPOs, this trend may be less significant and may need to incorporate successful acquired companies; and (3) relative successes may be driven by company sector/industry versus an overall comparison of all public companies.  In any case, it is clear that the use of visual network analysis provides a new way to predict success of IPO companies. When incorporated with analyst data, it can help guide an investor in choosing (or not choosing) a stock to add to their portfolio.  Look at the relational network, it could be golden!

Relational Map for HP’s Board of Directors Network
This LinkSViewer map shows HP’s network, including current and former Board Member connections, and active and inactive companies included. Note that this only includes Northern California companies. The Person nodes for Robert Knowling, Sam Ginn, Tom Perkins, and Robert Wayman are expanded. They are the only four current or former Board Members that link to other companies as well as HP. This means that even if all of the other Person nodes were expanded, no additional links would appear.

What does this map tell us?
1) Of seven former board members, only three (Knowling, Ginn, and Perkins) have other connections as board members in the Silicon Valley. This is a relatively low percentage and indicates a high probability that former HP board members move on to projects and positions outside of the Silicon Valley. Apart from those three, only the CFO (Robert Wayman) has any links to other Silicon Valley companies, and if those three former board members and Wayman are removed from the map, then HP is completely isolated with no connections to anything else in the valley. So any issue that arises is internally magnified because of the lack of correspondence as no channels exist for board knowledge to move to the outside world.
2) Although the portion of the network expanded through Knowling, Ginn, Perkins, and Wayman, does show some interesting overlaps and relationships, there is nothing particularly noteworthy in terms of patterns of connections. Furthermore, because three are former board members, it is even less likely that anything in that portion of the network has any real connection to the current situation on the HP board. Wayman’s board connections also dead end with the former board members links removed. So the effect is an isolation of the board as a result of insulation from outside ties. This is interesting because the nature of the insulation prevents communication to the outside by any normal channel. Thus, the only way for information to seep out is through a leak – perceived or otherwise.

The nature of the current controversy was caused by secrecy and subterfuge. This indicates that it may have more to do with internal dynamics of the board rather than the connections of the board to other external influences. If we were to remove the former board members from this map (including Knowling, Ginn, and Perkins), is almost a complete isolation from any contact to other boards within the Silicon Valley. The relative isolation of board members of such a large company from other connections in the valley could be attributed to the fact that HP is such a large company that taps corporate resources nationally and internationally. And this isolation makes HP susceptible to Groupthink.

Groupthink is a well-known group theory developed by Irving Janis to explain the faulty decision processes that often occur in small groups (e.g., Bay of Pigs, Watergate). Groupthink may explain contributing factors to the HP debacle (we’ll let the readers be the judges). Below are Janis’ 8 Symptoms of Groupthink:

1. Illusion of invulnerability – creates excessive optimism and encourages taking extreme risks.
2. Self-appointed mindguards – members who protect the group from adverse information that might shatter their shared complacency about the effectiveness and morality of their decisions.
3. Self-censorship – protection against deviations from the apparent group consensus, reflecting each member’s inclination to minimize the importance of their doubts and counterarguments
4. Outgroup stereotyping – enemy leaders are viewed as too evil to warrant genuine attempts to negotiate; or as too weak and stupid to counter whatever risky attempts are made to defeat their purposes
5. Belief in inherent morality - inclining the members to ignore the ethical or moral consequences of their decisions
6. Direct pressure on dissenters – any member who expresses strong arguments against any of the group’s stereotypes, illusions, or commitments is considered as contrary to what is expected from loyal members and pressured to conform
7. Collective rationalization – collective efforts to rationalize in order to discount warnings which might lead the members to reconsider their assumptions before they recommit themselves to their past decisions
8. Shared illusion of unanimity – concerning judgments conforming to the majority view (partly resulting from self-censorship of deviations, augmented by the false assumption that silence means consent)
Source: Janice, I. L. (1972). Victims of Groupthink. Boston, MA: Houghton Mifflin (pp. 197-8).

Researchers would claim that overly isolated and cohesive groups tend to have Groupthink. What happens, however, when the protective insulating layer around the group breaks down? At this point, the group is no longer cohesive and can implode upon its shroud of secrecy.

We invite opinions of others regarding whether this example typifies a Groupthink occurrence or not, and why. What do you think? Do you see other prominent examples of Groupthink happening today?

GroupScope is proud to announce the release of our LinkSViewer 1.2 product. LinkSViewer 1.2 is a visual network analysis tool that explores relationships between management teams, board members, investors, and companies in the Silicon Valley. It is the first application of the GroupScope network analysis engine, which is the result of research at Cornell University and our current headquarters in Shell Beach, California. LinkSViewer is made possible thanks to our partners at Link Silicon Valley (LinkSV.com), who develop the database used by our network engine. LinkSViewer turns that data into network maps that are easy to read and learn from. What you will discover are indirect relationships that may not be readily apparent without a network perspective. Using LinkSViewer 1.2 users can search, filter, summarize, email, and navigate any map to suit their specific needs.

There are several noteworthy features in our 1.2 product.
o People mapping based on management teams and boards of directors
o Filter and cross filter options for people, investors, and companies
o Advanced search and filter options such as amount and rounds of capital, active/inactive companies, current/former boards & management teams
o In-map legend and filter display for easy use of maps outside of the application
o Map zooming
o Link strength indicators and filters
o QuickSearch feature from any screen
o Add-a-node feature that lets you expand and compare multiple entities
o Person-Investor maps allowing indirect relationship views

We are offering LinkSViewer 1.2 on a trial basis for select users in the VC community. We are also happy to schedule demos in the Bay Area to showcase our map functionality. To obtain a trial password or schedule a demo, contact us at linksviewer@groupscope.com with your request and a little bit about your specific interests - perhaps some sample companies, people, and/or investors that are particularly relevant. Any other feedback is welcome - contact me if you have any other questions. Enjoy, and happy mapping.

As a child, I recall my grandmother reading Turkish coffee cup patterns flipped over on its saucer - blots indicated patterns that were read similar to tea leaves. These patterns had no contextual roots and seemed akin to fortune telling, but she was pretty darn accurate. How can we learn from patterned relationships using LinkSViewer Maps? There are two main points here: (1) Knowing how to select the right maps; (2) Knowing how to interpret these maps. Together this formula is a form of “capital fortune telling.”

LinkSViewer is designed to translate relational capital into $ in the investing world using network maps. Who are the best companies? Who are the best investors? What mergers and acquisitions are viable? This can be visualized using network analysis. Let’s look at a typical example:

Example: Google and Trapeze Networks Pattern Matching

Trapeze Networks is a startup company who was recently brought to our attention. As a network analyst, I examined Trapeze’s network maps using LinkSViewer and noted similar network structure patterns to Google. Two mapviews are of interest:

(1) Relational Maps: The prevalence of two well-connected investors

Recall that a relational map illustrates connections between a target company’s investors and their common investments. In the above maps the connectedness of Kleiner Perkins and Sequoia Capital (Google) vs. Accel Partners and Oak Investment Partners (Trapeze) indicate two strong players in early rounds of financing. Of particular interest are the green clusters in the above maps. Exploring further, I then clicked on the Company Map for Trapeze and Google.

(2) Company Maps: The similarity of strategic company clusters:

Google Company Map

Trapeze Company Map

Recall that a company map illustrates when companies have at least two common investors. In the above maps similar size and color network clusters indicate companies of similar ilk to the targeted companies. Trapeze’s cluster contains Synopsys, NET.com, Micro Linear, Netopia, & Polycom (Green = Public companies) and 2Wire, Big Bear Networks, Airgo Networks, Level 5 Networks, and Artimi (Blue = Private Companies). Google’s cluster consists of Redback Networks, Flextronics, InSite Vision, Electronic Arts, Onyx Pharmaceutical, Neo Magic, & Symantec (Green = Public Companies) and Shakti Systems, Stoke, PodShow, Digital Chocolate, & ConSentry Networks (Blue = Private Companies). We all know about the public companies, but how about the private companies in each cluster. Will they be successful? More specifically, will Trapeze be successful?

Now I’m no soothsayer, but is my job to point out network patterns. The way I see it, those who invest for a living are the true fortune tellers. Our role at GroupScope is to work with clients to help discover the meaning behind such map views. Any comments?

]]> http://linksviewer.com/blog/?feed=rss2&p=12 http://linksviewer.com/blog/?p=11 http://linksviewer.com/blog/?p=11#comments Fri, 14 Apr 2006 19:03:20 +0000 Dean Krikorian Company Analysis Network Theory https://scope.no-ip.org/blog/?p=11 This post is an example of a Company Analysis. The results indicate that Google is a high relational capital company based upon evidence of high company clustering and high investor clustering. The format of this post references applicable LinkSviewer maps for Google as well as other related companies and investors. Google Network maps are shown below and can be clicked on to enlarge and interact in a separate window:

Google has a dense relational network map. From this map, there are is high connectivity between investors (especially Sequoia & Kleiner Perkins). There are also key pathways between investors (all investors are connected beyond Google). More information can be gleaned from Company and Investor Maps.

Google has a well-clustered company map. There are two main company clusters for Google. A strong cluster based upon Kleiner Perkins & Sequoia and a Weak cluster from Angel Investors & Sequoia (See previous Relational Map). These clusters indicate private linked companies with Google potential: Stoke, Shakti, ConSentry, PodShow, Digital Chocolate (Strong Cluster); Vendio, Yodlee (Weak Cluster). Could these companies be the next Google? From an investor network standpoint they have similar backing structures and board direction from key investors. Public companies with upside: Insite, Electronic Arts, Onyx, Symantec, Redback, Flextronics, NeoMagic (Strong Cluster); RedEnvelope (Weak Cluster). These companies preceded Google’s IPO in going public and could further gain from association with Google as partners and/or customers.

Google has a well-clustered investor map. There is high connectivity between all investors in this map (as noted in the previous Relational Map). Strong investor ties are identified in this map as dark lines between investors (i.e., more than 2 common investments). Kleiner Perkins & Sequoia; Kleiner Perkins & Ram Shriram; and Angel Investors & Sequoia all have strong ties to one another. Here we gain the extent to which these investors invest together. Particularly notable is the connectivity of individual investors tied to larger investors: Ram Shriram to Kleiner Perkins; Ram Shriram to Sequoia; Andy Bechtolscheim to Kleiner Perkins; Andy Bechtolscheim to Angel Investors. This indicates a Strength of Weak Ties phenomenon because individuals do not have as many investments as large VC firms. Individual investor presence may indicate a key leadership role in distinguishing the company, particularly if tied to the resources of a large VC firm.

Potential next steps: (1) Map relational networks of multiple companies (Strong/Weak Cluster companies); (2) Map relational network of multiple investors (Strong tie investors/Well-connected individual investors).

]]> http://linksviewer.com/blog/?feed=rss2&p=11 http://linksviewer.com/blog/?p=10 http://linksviewer.com/blog/?p=10#comments Wed, 12 Apr 2006 21:52:36 +0000 Dean Krikorian General Network Theory https://scope.no-ip.org/blog/?p=10 As I examine countless Relational Maps on LinkSViewer, I notice patterns in various companies and investors. This calls to mind a basic premise of Relational Capital as the value-added function of network ties. But what precisely is Relational Capital and how can one go about finding it?

From a social network perspective, the similar concept of Social Capital is defined by Nan Lin (2001) as “Investment in social relations with expected returns in the marketplace.” Social capital is said to form the basis of democratic society, whereby humans offer their ties to others as a means of goodwill and reciprocated exchange. There are certainly a myriad of definitions of social capital and as of late the term has been watered down to include virtually anything related to social networks that has a value-added component. For example, social capital has been popularized by Robert Putnam’s “Bowling Alone” (2000) hypothesis which purports that social capital in the form of civic engagement is on the decline in the US. More recently, social networking on the Internet has reinvigorated the spirit of social capital, albeit in a different form: Relationship Capital Management.

Wikipedia defines Relationship Capital Management as “a class of business solutions and software applications and services which help individuals and organizations to identify, manage and leverage their network of business and professional relationships as assets.” Focusing on the network itself can presumably yield insight into network assets. Furthermore, “Relationship Capital Management systems are sometimes referred to as applications which provide relationship intelligence, mapping or development integrated with Customer Relationship Management systems.” Here one can get a sense of how network tools such as Spoke and Visible Path can make sense of a jumble of customer contacts. But does this definition encapsulate the general sense of relational capital? I think not, because there are many types of relationships that are yet to be (1) contextualized; (2) mapped; and (3) interacted upon. Upon discussion of the above three points, it should be of no surprise that the ensuing definition of Relational Capital differs from Social Capital and Relationship Capital Management. The below definition is meant to be more specific, more pragmatic, and more measurable/tangible.

Relational Capital Definition:
The value added worth of a network. It can be thought of as the strategic advantage afforded by key relational ties. In general there are three stages of relational capital that ensure its value (in increasing order):
(1) Context: The applied value of a network

(2) Visualization: The illustrated value of a network

(3) Interaction: The personalized or shared value of a network

Example: LinkSViewer Relational Capital
(1) Context: Companies and investors (bipartite network) in the Bay Area via LinkSV database
(2) Visualization: Colors (Status), Shades (Activity), Views (Relational, Company, Investor, Complete)
(3) Interaction: Filters, Search, Email Maps, Info. Window contents

(1) Contextual Relational Capital:
The first wave of social networking software exemplifies the multiple ways to contextualize network relationships on the web – this is the tip of the iceberg. For example, one may use myspace.com if you want a date, facebook.com to keep in touch with classmates, craigslist.com to buy goods, or countless other flavor of the month social networking sites to capitalize on relationships. Don’t get me wrong, all of these sites are important because they provide separate contextual forms of relational capital and more (not less) of these types of services are on the horizon. Yet, the question remains, what specific value do such sites provide to their users? My sense is that one needs to understand more about how these relationships can directly yield value before they reach their full potential. On the one hand, this is why the idea of a Spoke or Visible Path makes perfect sense because sales contacts can be easily translated into dollars. But actually mining these relationships is a more difficult task because it takes individuals familiar with network analysis to glean relationship capital from a network. One way to ease this burden is to design in features allowing users to see their networks through a relational capital lens.

(2) Visual/Map Relational Capital:
The everyday user implicitly understands the importance of their relationships. Whether or not they realize the importance of hubs, boundary spanners, liaisons, or other social network terminology is really not the point – network structures yield contextual insight. Barring a course on social network analysis, how can the everyday user read into their networks? The simple answer is to give them a picture of their network: clusters emerge, isolates are revealed, hubs form spokes, and liaisons connect. The more complicated answer is to know what to look for in a network, structurally speaking. In this case the network analyst needs to be able to design in key network indices that are visually seamless to the end user. Thus, determining what pictures to map is a complex task that may not even be recognized by the user, but takes foresight from the designer. For example, the size of a network map becomes difficult to manage beyond the Dunbar number of 150 nodes: how can one visualize large maps? (e.g., Relational Map solution in LinkSViewer). More practically, the designer must know beforehand what contextual relationships matter, thereby ensuring that there is implicit value to the end user. For example, the coloring of nodes should indicate a true “at-a-glance” relationship for the user (e.g., status of companies or investors in LinkSViewer). By designing in network features visually, the designer saves the end user’s time and effort because people tend to be more visual and would rather not read a mathematical textbook. How these visual features are actually used involves following user interactions in a typical setting.

(3) Interactive Relational Capital:
The ultimate goal in this process is to make each network personalizable to the end user(s). In this way, relational capital is highly contextualized because each map is created and therefore has meaning to individual users (my relational capital is different than your relational capital) and groups of users (our relational capital is greater than the sum of our individual uses).

By interacting with a network, an individual has the ability to specify what is in and out of a network, how it should be illustrated, and how greater details of each node can be accessed by the end user. For example, the ability to search and filter networks in LinkSViewer underscores a tool that personalizes relevant information as relational capital - people only care about what they are interested in: nothing more, nothing less. As we all know from Google, a network browsing feature is also important - the ability to map “on-the-fly” allows for discovery across networks and yields “Eurekas” impossible from a non-interactive (static) map.

From a group perspective, the ability to collaborate about a network and share map images can be a common theme of discussion or perhaps yield new ideas from different perspectives. For example, the email map function of LinkSViewer allows for users to share, annotate, and interact about a common company or investor view. As a result, this form of interaction allows for community building (more on this in a future post).

These functions are at the cusp of innovation and there are certainly several other ways to make network maps more interactive. Most of this becomes salient as users are interviewed as to their actual click-by-click experience using relational network tools such as LinkSViewer.

Conclusion:
Relational Capital is a relatively complex phenomenon found in multiple forms, disciplines, and meanings, generally based on the concept of valued-added ties that bind. Despite prior equivocality, the goal here has been to highlight key research and applications in generating a more specific, pragmatic, and measurable definition. The definition here posits three increasing stages that ensure Relational Capital: (1) Context: Applied value networks; (2) Visualization/Mapping: Illustrated value networks; and (3) Interaction: Personalized or shared value networks. LinkSViewer provides an example of how Relational Capital can be utilized in an everyday context. Strategic advantages of this approach to Relational Capital provide potential ways to differentiate product placement and protect intellectual property. The ultimate goal involves teaching everyday people about how to better utilize their networks as Relational Capital – without taking a course in social network analysis or graph theory.

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