Clustering Connections with LinkedIn InMaps

Last month, LinkedIn announced a new application called InMaps which can be used to visualize a LinkedIn Network. LinkedIn’s aim is to enable its users to see what their network looks like and so better leverage their network, including identifying areas where it could be strengthened and extended.

As readers of this blog will know, data visualization is something in which we are keenly interested and so we went to try it out. Curiously, LinkedIn does not promote its labs area – or at least not that we could tell – even though there are some very interesting experimental applications in it (e.g. try out INFINITY ).

For our evaluation, we chose a relatively small network to evaluate because we were interested in exploring the representation in some depth. (Note: we have read comments from others that the software may be challenged dealing with very large networks in the 30,000+ region. D.J. Patil, Chief Scientist of LinkedIn notes the same in his comments on a posting on the FlowData blog: http://flowingdata.com/2011/01/24/explore-your-linkedin-network-visually-with-inmaps/#comment-63891 ).

It is recommended that InMaps is used with Firefox or Chrome rather than IE. Once you have reached the Labs page and selected the InMaps option, all you need to do is to permit the InMaps application to access your LinkedIn Connections. The application then processes LinkedIn’s connection-network representation and produces a diagram which is not dissimilar in style to Gephi (see previous blog posting: http://ichromatiq.blogspot.com/search/label/Gephi ) and indeed LinkedIn Maps is listed on Gephi’s own web site as a user of the Gephi toolkit (see: http://gephi.org/2011/happy-new-year/ )

Example of a LinkedIn InMap

Highly connected individuals within your network are represented with larger nodes and fonts. It is important to bear in mind, however, that the map is only representing the connectedness between the individuals to which you are connected. It is not showing the connectedness of those individuals within LinkedIn. So, for example, if you have a connection to individual A who happens to have a very large LinkedIn network but, for some reason, no one else in your network is connected to them, they will appear as a small node with a single link to you. If, on the other hand, you are connected to individual B who is connected to all the same people with whom you are connected, that individual is going to be represented as a large node.

We particularly liked the fact that the map highly interactive. Not only can you pan, zoom and mouse-over a node to get tool-tip information, but clicking on the node brings up their LinkedIn profile in the right hand sidebar. Very useful!

Most intriguing however is the clustering, represented by different colors. InMaps allows you to choose your own label for each cluster/color but gives little information as to how the clusters are derived except to say that they represent different affiliations such as previous employers, educational institutions or industries. Looking at the inMap shown here, it was clear that the dominating factor in the clustering was employment attribute and specifically company name.

Close-Up of "Misc" Cluster

The small red cluster on the immediate left of center is essentially a “misc” group. Looking at this in more detail, we noticed that connections based on professional organizations did not seem to be picked up – but that may have been either because the number of such connections was below the clustering threshold and/or the individuals concerned had not recorded the organization in their profile. We also noticed that one particular employer affiliation had not been clustered. In this case, the reason we believe is that this particular enterprise was so large that people often reference the operating division in which they work rather than the whole. Further the name of the enterprise has changed over the years. Since it would be an enormous task to keep track of all the changes – name and organizational structure – many Fortune 5000 companies go through, it might be useful to allow users to overlay the initial map with affiliations they know exist i.e. adding additional attributes.

We would have liked to have compared the representation produced by inMaps with those produced by other visualization tools: in particular NodeXL because that would have allowed us to add/modify attributes easily. Unfortunately while it is possible to export out your LinkedIn connections, you cannot access the connections between individual s in your network.

Overall, this is a very useful visualization tool, providing valuable insight into one’s professional network. It would be very interesting to overlay this with other perspectives including email traffic flow or twitter activity to give an extended picture of how one communicates and connects within the business and professional environment. More please!

Visualizing Email Communications

Visualization of an email communication network

Social network analysis software enables the interactions and relationships between individuals or organizations to be modeled and visualized in a graphical format with individuals/organizations are represented as nodes and interactions/relationships as edges. In recent months, such software has been used extensively to model and analyze behavior in social spaces such as Facebook, Twitter etc. It seemed to us that it might also be useful in analyzing and understanding communication patterns in emails.

Emails are a key source of information in litigations (witness the publication of significant emails in the recent Goldman Sachs case) and are also monitored for compliance reasons in regulated industries. While most research of email data involves some form of keyword searching, there are occasions when it is important to understand who is in communication with whom: particularly if an investigation is at an early stage and may need to be broadened.

Understanding patterns of communication (as evidenced by email traffic) is also important when investigating why projects are failing or teams are not performing effectively. There is a substantial body of research that shows that communication issues are one of the primary reasons behind failing projects and dysfunctional teams. Analyzing and understanding the pattern of communications within a team or department can help business leaders and project managers identify where the breakdowns are occurring and target remedial action.

Gephi is an open source tool for visualizing networks (http://www.gephi.org/). It runs on Windows, Linux and Macs. While it will import files in a variety of formats (including CSV), the recommended format for importing data is .gexf – graph exchange XML format (see: http://gexf.net/format/index.html). GEXF is an XML based file format that is straightforward to generate once basic email metadata has been extracted and stored in a SQL database.

Some of the visualizations we were able to generate from an anonymized email set using this procedure are shown below. Gephi is very flexible in allowing for a range of different network representations and filtering so, for example, only highly connected individuals are shown. On the downside, it is still in alpha and, from our experience, not particularly robust. It crashed several times while we were attempting simple operations like adding text labels. While easy to import and export data, to use it effectively, some knowledge of the mathematics behind graphing and network analysis is helpful.

Visualization of Key Communicators in an Email Network

Use of Color to Show SubGroups within an Email Communication Network

Close-Up Showing Degree of Communication (as Line Thickness) between Participants in an Email Network