Fast Algorithms for Intimate-Core Group Search in Weighted Graphs

TL;DR

This paper introduces LEKS, an efficient algorithm for finding intimate-core groups in weighted graphs by connecting query nodes with a small-weighted spanning tree and expanding it to a connected k-core, outperforming existing methods.

Contribution

The paper presents LEKS, a novel local exploration framework that efficiently finds intimate-core groups in large weighted graphs, improving over traditional maximal k-core refinement methods.

Findings

LEKS significantly reduces computation time on large networks.

The method effectively finds smaller, more relevant communities.

Experimental results validate superior efficiency and accuracy.

Abstract

Community search that finds query-dependent communities has been studied on various kinds of graphs. As one instance of community search, intimate-core group search over a weighted graph is to find a connected $k$-core containing all query nodes with the smallest group weight. However, existing state-of-the-art methods start from the maximal $k$-core to refine an answer, which is practically inefficient for large networks. In this paper, we develop an efficient framework, called local exploration k-core search (LEKS), to find intimate-core groups in graphs. We propose a small-weighted spanning tree to connect query nodes, and then expand the tree level by level to a connected $k$-core, which is finally refined as an intimate-core group. We also design a protection mechanism for critical nodes to avoid the collapsed $k$-core. Extensive experiments on real-life networks validate the effectiveness and efficiency of our methods.