article

A heuristic local community detection method (HLCD)

Authors:

Mohammad Ali Tabarzad,

Ali HamzehAuthors Info & Claims

Applied Intelligence, Volume 46, Issue 1

Pages 62 - 78

https://doi.org/10.1007/s10489-016-0824-9

Published: 01 January 2017 Publication History

Abstract

The advances in social networks has led to the concentration of research on analyzing people's behaviors in these networks. Accordingly, detecting communities and the interactions between their members is one of the most important issues addressed by these studies. After the proposition of new community detection methods in recent years, due to the extensive volume of the information generated in social networks and the increasing growth in the size of these networks, researchers became more interested in local, rather than global, detection methods. This paper proposes a heuristic approach to detecting communities by investigating local information. Comparing this method with state-of-the-art approaches, it is observed that the proposed approach outperforms the compared methods in detecting communities and their members and provides more accurate results.

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Cited By

Samie MBehbood EHamzeh A(2023)Local community detection based on influence maximization in dynamic networksApplied Intelligence10.1007/s10489-022-04403-553:15(18294-18318)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1007/s10489-022-04403-5
Jia JLiu PDu XYao YLei Z(2022)The overlapping community discovery algorithm based on the local interaction modelIntelligent Data Analysis10.3233/IDA-21575726:1(153-171)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDA-215757
Bahadori SZare HMoradi P(2021)PODCDExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114650174:COnline publication date: 15-Jul-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114650
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Published In

cover image Applied Intelligence

Applied Intelligence Volume 46, Issue 1

January 2017

240 pages

ISSN:0924-669X

Issue’s Table of Contents

Copyright © Copyright © 2017 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2017

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Cited By

Samie MBehbood EHamzeh A(2023)Local community detection based on influence maximization in dynamic networksApplied Intelligence10.1007/s10489-022-04403-553:15(18294-18318)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1007/s10489-022-04403-5
Jia JLiu PDu XYao YLei Z(2022)The overlapping community discovery algorithm based on the local interaction modelIntelligent Data Analysis10.3233/IDA-21575726:1(153-171)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDA-215757
Bahadori SZare HMoradi P(2021)PODCDExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114650174:COnline publication date: 15-Jul-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114650
Bahadori SMoradi PZare H(2021)An improved limited random walk approach for identification of overlapping communities in complex networksApplied Intelligence10.1007/s10489-020-01999-451:6(3561-3580)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10489-020-01999-4
Li XTian QTang MChen XYang X(2020)Local community detection for multi-layer mobile network based on the trust relationWireless Networks10.1007/s11276-019-01938-326:8(5503-5515)Online publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1007/s11276-019-01938-3
Guo KHe LChen YGuo WZheng J(2019)A local community detection algorithm based on internal force between nodesApplied Intelligence10.1007/s10489-019-01541-150:2(328-340)Online publication date: 24-Jul-2019
https://dl.acm.org/doi/10.1007/s10489-019-01541-1
Ding XZhang JYang J(2018)A robust two-stage algorithm for local community detectionKnowledge-Based Systems10.1016/j.knosys.2018.04.018152:C(188-199)Online publication date: 15-Jul-2018
https://dl.acm.org/doi/10.1016/j.knosys.2018.04.018
Said AAbbasi RMaqbool ODaud AAljohani N(2018)CC-GAApplied Soft Computing10.1016/j.asoc.2017.11.01463:C(59-70)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.asoc.2017.11.014
Ding FZhuang Y(2018)Ego-network probabilistic graphical model for discovering on-line communitiesApplied Intelligence10.1007/s10489-018-1137-y48:9(3038-3052)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10489-018-1137-y
Tabarzad MHamzeh A(2018)Incremental community miner for dynamic networksApplied Intelligence10.1007/s10489-017-1134-648:10(3372-3393)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10489-017-1134-6
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