Effective and Efficient Core Computation in Signed Networks

TL;DR

This paper extends the k-core concept to signed networks with (p,n)-core, providing efficient algorithms for its computation despite NP-hardness, and demonstrates their effectiveness on real and synthetic data.

Contribution

It introduces the (p,n)-core model for signed networks and proposes three efficient algorithms to compute it, addressing NP-hardness.

Findings

Proposed algorithms outperform existing methods in efficiency.

Validated algorithms on real-world and synthetic networks.

Demonstrated the effectiveness of (p,n)-core in signed network analysis.

Abstract

With the proliferation of mobile technology and IT development, people can use social network services at any place and anytime. Among many social network mining problems, identifying cohesive subgraphs attract many attentions from different fields due to its numerous applications. Among many cohesive subgraph models, k-core is the most widely used model due to its simple and intuitive structure. In this paper, we formulate (p,n)-core in signed networks by extending k-core. (p,n)-core simultaneously guarantees sufficient internal positive edges and deficient internal negative edges. We formally prove that finding an exact (p,n)-core is NP-hard. Hence, we propose three efficient and effective algorithms to find a solution. Using real-world and synthetic networks, we demonstrate the superiority of our proposed algorithms.