Abstract: The present disclosure provides a new framework and associated techniques, referred to herein as “ego-splitting,” that enable the detection of clusters in graphs that are descriptive of networks, including highly complex networks. Ego-splitting leverages local structures within a graph known as ego-nets to de-couple overlapping clusters. For example, an ego-net can be the subgraph induced by the neighborhood of each node. Ego-splitting is a highly scalable and flexible framework, with provable theoretical guarantees. Ego-splitting reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (also known as partitioning) problem. Ego-splitting enables the scaling of community detection to graphs with tens of billions of edges and outperforms previous solutions.

Abstract: The present disclosure provides a new framework and associated techniques, referred to herein as “ego-splitting,” that enable the detection of clusters in graphs that are descriptive of networks, including highly complex networks. Ego-splitting leverages local structures within a graph known as ego-nets to de-couple overlapping clusters. For example, an ego-net can be the subgraph induced by the neighborhood of each node. Ego-splitting is a highly scalable and flexible framework, with provable theoretical guarantees. Ego-splitting reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (also known as partitioning) problem. Ego-splitting enables the scaling of community detection to graphs with tens of billions of edges and outperforms previous solutions.