AI

Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters

Abstract

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.