Community Analysis in Complex Networks: Unfolding Structure and Semantics
The goal of community detection is to identify densely-connected units within large networks. An implicit assumption is that all the constituent nodes belong equally to their associated community. However, some nodes are more important in the community than others. To date, efforts have been primarily made to identify communities as a whole, rather than understanding to what extent an individual node belongs to its community. Therefore, most metrics for evaluating communities (e.g., modularity) are global. These metrics produce a score for each community, not for each individual node. In the first half of my talk, I will discuss our hypothesis that the membership of nodes in a community is not uniform, and quantify a new vertex-based local metric called “permanence”. The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon two factors: (i) the extent of connections of the vertex within its community versus outside its community, and (ii) how tightly the vertex is connected internally. I will also discuss how permanence can help understand and utilize the structure of communities, and how it can be leveraged to design different real-world applications.
In the second half of my talk, I will present a general overview of other research activities in my lab at IIIT Delhi.