Community Detection in Complex Networks based on Improved Genetic Algorithm and Local Optimization

Abstract

This paper proposes the community detection in complex networks based on improved genetic algorithm and local optimization (IGALO)

Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

“…The main philosophy behind this proposal is to use a social network analysis measure to generate the initial population [177]. Another GA-based approach is presented in [39] for similar purposes, in which authors adopt label propagation for creating the initial population, and conduct an anti-destructive one-way crossover. Moreover, for improving the search efficiency, authors implement a node-local optimization strategy as a means to perform a tailored mutation process over evolved solutions.…”

Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks

“…HSCDA is applied to four real networks, respectively; the average of optimal solutions of HSCDA after running 30 times is recorded. Table 2 lists comparison results between HSCDA and GN, FN, and BGLL algorithm in terms of NMI, where the results of GN, FN, and BGLL are taken from [25]. As seen from the table, the NMIs of HSCDA are superior to other three algorithms except that NMIs are the same as BGLL in Football and Polbooks.…”