Approximating the Community Structure of the Long Tail

February 18th, 2008

Social Networks and Web graphs exhibit certain typical properties. The classic work by Barabási–Albert showed how nodes in such network link preferentially — popular nodes often gain disproportionately larger share of the links. This is also known in other fields as the 80/20 rule or simply the “rich get richer phenomenon“. Another early work by Steve Borgatti studied social networks and found that they exhibit a core-periphery property. A small set of (popular) nodes form the core and the rest comprise of the peripheral nodes. To the best of my knowledge, community detection algorithms have often worked independent of such underlying network properties.

I have been exploring an idea that can utilize the core-periphery structure of social networks to approximately compute the communities in the graph. The intuition behind this method is really quite simple. The basic idea boils down to the following:

“The core of the social network typically defines the communities present in it. By looking at the link structure of the core and identifying how the rest of the network connects to the core we can efficiently compute communities in large graphs.”

This idea can be easily explained by considering the following network of email communication (obtained from Dr. Mark Newman’s site). The original adjacency matrix was permuted to order the nodes based on their degree. Thus the core is represented by submatrix A which is quite dense. The submatrix B, here corresponds to how the rest of the network links to its core. The submatrix C is a very sparse matrix that consists of links between nodes in the long tail. Since C is quite sparse, it can be ignored without much degradation of the clustering/community detection results. Thus it leads to saving a significant amount of computation and storage. By utilizing just the core of the social network (matrix A) and how other nodes link to the core (matrix B) we can approximate the overall community structure of the entire graph, much more efficiently.

The rest boils down the to the mathematical formulation of the above idea using Spectral clustering techniques. You can read more about it in my poster paper that was recently accepted to ICWSM. (A Tech Report version with a more detailed analysis would be available shortly)