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!