Community Detection in Signed Networks: an Error-Correcting Code Approach

TL;DR

This paper models community detection in signed networks as an error-correcting code decoding problem, introducing a new Hamming distance algorithm that outperforms existing decoding-based methods.

Contribution

It establishes a novel connection between signed network community detection and error-correcting code decoding, proposing a new Hamming distance algorithm for improved performance.

Findings

The Hamming distance algorithm outperforms bit-flipping and belief propagation algorithms.

Community detection can be effectively formulated as decoding in error-correcting codes.

Experimental results validate the superiority of the proposed method.

Abstract

In this paper, we consider the community detection problem in signed networks, where there are two types of edges: positive edges (friends) and negative edges (enemies). One renowned theorem of signed networks, known as Harary's theorem, states that structurally balanced signed networks are clusterable. By viewing each cycle in a signed network as a parity-check constraint, we show that the community detection problem in a signed network with two communities is equivalent to the decoding problem for a parity-check code. We also show how one can use two renowned decoding algorithms in error- correcting codes for community detection in signed networks: the bit-flipping algorithm, and the belief propagation algorithm. In addition to these two algorithms, we also propose a new community detection algorithm, called the Hamming distance algorithm, that performs community detection by finding a codeword that minimizes the Hamming distance. We compare the performance of these three algorithms by conducting various experiments with known ground truth. Our experimental results show that our Hamming distance algorithm outperforms the other two.