Resampling community detection to maximize propagation in complex network

TL;DR

This paper introduces RCD-Map, a penalized resampling community detection method that enhances node ranking strategies to maximize information propagation in complex networks, validated through SIR model simulations.

Contribution

The paper proposes a novel penalized resampling approach, RCD-Map, that improves existing node ranking methods by incorporating community information to better maximize propagation.

Findings

RCD-Map outperforms baseline methods in propagation range.

Penalized resampling reduces biases in community detection.

Enhanced methods show improved propagation in simulations.

Abstract

Identifying important nodes in complex networks is essential in theoretical and applied fields. A small number of such nodes have deterministic power to decide information spreading, so it is of importance to find a set of nodes that maximize the propagation in networks. Based on baseline ranking methods, various improved methods were proposed, but there does not exist one enhanced method that covers all the base methods. In this paper, we propose a penalized method called RCD-Map, which is short for resampling community detection to maximize propagation, on five baseline ranking methods(Degree centrality, Closeness centrality, Betweennees centrality, K-shell and PageRank) with nodes' local community information. We perturbed the original graph by resampling to decrease the biases and randomness brought by community detection methods-both overlapping and non-overlapping methods. To assess the performance of our identifying method, SIR(susceptible-infected-recovered) model is applied to simulate the information propagation process. The result shows that methods with penalties perform better with a vaster propagation range in general.