Approximate Closest Community Search in Networks
Xin Huang, Laks V.S. Lakshmanan, Jeffrey Xu Yu, Hong Cheng

TL;DR
This paper introduces an NP-hard problem of finding the closest k-truss community containing query nodes, and proposes approximation algorithms with practical efficiency demonstrated through experiments.
Contribution
It formulates the closest truss community search problem, proves its NP-hardness, and develops efficient approximation algorithms with experimental validation.
Findings
The problem is NP-hard and hard to approximate within a factor of (2-ε).
Proposed algorithms achieve 2-approximation and are efficient on real networks.
Experimental results confirm the effectiveness of the algorithms in practice.
Abstract
Recently, there has been significant interest in the study of the community search problem in social and information networks: given one or more query nodes, find densely connected communities containing the query nodes. However, most existing studies do not address the "free rider" issue, that is, nodes far away from query nodes and irrelevant to them are included in the detected community. Some state-of-the-art models have attempted to address this issue, but not only are their formulated problems NP-hard, they do not admit any approximations without restrictive assumptions, which may not always hold in practice. In this paper, given an undirected graph G and a set of query nodes Q, we study community search using the k-truss based community model. We formulate our problem of finding a closest truss community (CTC), as finding a connected k-truss subgraph with the largest k that…
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Taxonomy
TopicsCaching and Content Delivery · Complex Network Analysis Techniques · Optimization and Search Problems
