Effective semisupervised community detection using negative information
type: Item
instance of: scholarly article
title: Effective semisupervised community detection using negative information
zbMATH Open document ID: 1395.91374
DOI: 10.1155/2015/109671
author: Dong Liu
author: Dequan Duan
author: Shikai Sui
author: Guojie Song
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.
Mathematics Subject Classification ID: 91D30
zbMATH DE Number: 6925501
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/109671
OpenAlex ID: W2020356501
Wikidata QID: Q59118434
cites work: Community structure in social and biological networks
cites work: Fast unfolding of communities in large networks
cites work: Detecting network communities: a new systematic and efficient algorithm
description: scientific article; zbMATH DE number 6925501