The Web, society, information, cells and brain can all be represented and studied as complex networks of interactions. Nodes in such networks tend to organize into clusters and communities, which represent the fundamental structures for understanding the organization of complex systems. Even though detection of network communities is of significant importance for computer science, sociology and biology, our understanding of the community structure of large networks remains limited.

We study a set of more than 200 large networks with the goal to understand and identify communities in networks. We challenge the conventional view of network community structure and show that it is not exhibited by the large real-world networks. We then present a new conceptual model of network community structure, which reliably captures the overall structure of networks and accurately identifies the overlapping nature of network communities.

This is joint work with Jaewon Yang.