Mining Contrasting Quasi-Clique Patterns

TL;DR

This paper introduces contrasting quasi-clique patterns to identify vertex groups that are dense in one graph but sparse in another, aiding knowledge extraction from multi-graph data.

Contribution

It proposes a novel model and efficient algorithm for mining contrasting quasi-clique patterns, highlighting differences between graphs.

Findings

Effective in identifying contrasting dense and sparse regions

Applicable to synthetic and real-world datasets

Enables new insights in multi-graph analysis

Abstract

Mining dense quasi-cliques is a well-known clustering task with applications ranging from social networks over collaboration graphs to document analysis. Recent work has extended this task to multiple graphs; i.e. the goal is to find groups of vertices highly dense among multiple graphs. In this paper, we argue that in a multi-graph scenario the sparsity is valuable for knowledge extraction as well. We introduce the concept of contrasting quasi-clique patterns: a collection of vertices highly dense in one graph but highly sparse (i.e. less connected) in a second graph. Thus, these patterns specifically highlight the difference/contrast between the considered graphs. Based on our novel model, we propose an algorithm that enables fast computation of contrasting patterns by exploiting intelligent traversal and pruning techniques. We showcase the potential of contrasting patterns on a variety of synthetic and real-world datasets.