We propose a new framework called Ego-splitting for detecting clusters in complex
networks which leverage the local structures known as ego-nets (i.e. the subgraph
induced by the neighborhood of each node) to de-couple overlapping clusters.
Ego-splitting is highly scalable and flexible framework, with provable theoretical
guarantees, that reduce the complex overlapping clustering problem to a simpler and
more amenable non-overlapping (partitioning) problem. We can solve community
detection in graphs with tens of billions of edges and outperform previous
solutions based on ego-nets analysis. More precisely, our framework works in two
steps: a local ego- net analysis, and a global graph partitioning. In the local
step, we first partition the nodes’ ego-nets using non-overlapping clustering. We
then use these clusters to split each node of the graph into its persona nodes that
represents the instantiation of the node in its communities. Then, in the global
step, we partition these new persona nodes to obtain an overlapping clustering of
the original graph.