Diversified Coherent Core Search on Multi-Layer Graphs

TL;DR

This paper introduces a new dense subgraph model called d-coherent core for multi-layer graphs, along with algorithms that efficiently find large, diverse dense subgraphs surpassing traditional quasi-cliques in size and coverage.

Contribution

The paper proposes the d-coherent core model and develops algorithms with provable approximation ratios for diversified dense subgraph search on multi-layer graphs.

Findings

Algorithms are faster than greedy methods.

Algorithms produce results comparable to greedy approaches.

D-coherent cores detect larger dense subgraphs than quasi-cliques.

Abstract

Mining dense subgraphs on multi-layer graphs is an interesting problem, which has witnessed lots of applications in practice. To overcome the limitations of the quasi-clique-based approach, we propose d-coherent core (d-CC), a new notion of dense subgraph on multi-layer graphs, which has several elegant properties. We formalize the diversified coherent core search (DCCS) problem, which finds k d-CCs that can cover the largest number of vertices. We propose a greedy algorithm with an approximation ratio of 1 - 1/e and two search algorithms with an approximation ratio of 1/4. The experiments verify that the search algorithms are faster than the greedy algorithm and produce comparably good results as the greedy algorithm in practice. As opposed to the quasi-clique-based approach, our DCCS algorithms can fast detect larger dense subgraphs that cover most of the quasi-clique-based results.