Improving temporal smoothness and snapshot quality in dynamic network community discovery using NOME algorithm.

ABSTRACT: The goal of dynamic community discovery is to quickly and accurately mine the network structure for individuals with similar attributes for classification. Correct classification can effectively help us screen out more desired results, and it also reveals the laws of dynamic network changes. We propose a dynamic community discovery algorithm, NOME, based on node occupancy assignment and multi-objective evolutionary clustering. NOME adopts the multi-objective evolutionary algorithm MOEA/D framework based on decomposition, which can simultaneously decompose the two objective functions of modularization and normalized mutual information into multiple single-objective problems. In this algorithm, we use a Physarum-based network model to initialize populations, and each population represents a

SUBMITTER: Cai L 

PROVIDER: S-EPMC10403191 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature