Finding the Hierarchy of Dense Subgraphs using Nucleus Decompositions

TL;DR

This paper introduces nucleus decomposition, a hierarchical graph structure that identifies overlapping dense subgraphs efficiently, generalizing existing methods like k-cores and k-truss, and providing a comprehensive view of dense substructures.

Contribution

It proposes a novel nucleus decomposition framework, algorithms for its computation, and demonstrates its effectiveness and scalability on large real-world graphs.

Findings

Nucleus decomposition generalizes k-core and k-truss.

The hierarchy reveals dense subgraph relationships.

Algorithm processes graphs with millions of edges in under an hour.

Abstract

Finding dense substructures in a graph is a fundamental graph mining operation, with applications in bioinformatics, social networks, and visualization to name a few. Yet most standard formulations of this problem (like clique, quasiclique, k-densest subgraph) are NP-hard. Furthermore, the goal is rarely to find the "true optimum", but to identify many (if not all) dense substructures, understand their distribution in the graph, and ideally determine relationships among them. Current dense subgraph finding algorithms usually optimize some objective, and only find a few such subgraphs without providing any structural relations. We define the nucleus decomposition of a graph, which represents the graph as a forest of nuclei. Each nucleus is a subgraph where smaller cliques are present in many larger cliques. The forest of nuclei is a hierarchy by containment, where the edge density increases as we proceed towards leaf nuclei. Sibling nuclei can have limited intersections, which enables discovering overlapping dense subgraphs. With the right parameters, the nucleus decomposition generalizes the classic notions of k-cores and k-truss decompositions. We give provably efficient algorithms for nucleus decompositions, and empirically evaluate their behavior in a variety of real graphs. The tree of nuclei consistently gives a global, hierarchical snapshot of dense substructures, and outputs dense subgraphs of higher quality than other state-of-the-art solutions. Our algorithm can process graphs with tens of millions of edges in less than an hour.