Online Sparsification of Bipartite-Like Clusters in Graphs

TL;DR

This paper introduces efficient online algorithms for identifying bipartite-like clusters in large graphs, improving speed while maintaining clustering quality in both synthetic and real-world datasets.

Contribution

It presents novel online sparsification algorithms specifically designed for bipartite-like clusters in undirected and directed graphs, enhancing computational efficiency.

Findings

Algorithms significantly speed up existing clustering methods

Effective in both synthetic and real-world datasets

Preserve clustering quality while reducing computational cost

Abstract

Graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science. While the objective of most graph clustering algorithms is to find a vertex set of low conductance, a sequence of recent studies highlights the importance of the inter-connection between vertex sets when analysing real-world datasets. Following this line of research, in this work we study bipartite-like clusters and present efficient and online sparsification algorithms that find such clusters in both undirected graphs and directed ones. We conduct experimental studies on both synthetic and real-world datasets, and show that our algorithms significantly speedup the running time of existing clustering algorithms while preserving their effectiveness.