research-article

DynamicNet: an effective and efficient algorithm for supporting community evolution detection in time-evolving information networks

Authors:

Alfredo Cuzzocrea,

Francesco Folino,

Clara PizzutiAuthors Info & Claims

IDEAS '13: Proceedings of the 17th International Database Engineering & Applications Symposium

Pages 148 - 153

https://doi.org/10.1145/2513591.2513658

Published: 09 October 2013 Publication History

Abstract

DynamicNet, an effective and efficient algorithm for supporting community evolution detection in time-evolving information networks is presented and experimentally evaluated in this paper. DynamicNet introduces a graph-based model-theoretic approach to represent time-evolving information networks, and to capture how they change over time. A central feature of DynamicNet is represented by the ability of supporting matching-based community evolution detection, by identifying several classes of community transitions. Experimental results clearly demonstrate the reliability and the efficiency of our proposal.

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Cited By

Khafaei TTavakoli Taraghi AHosseinzadeh MRezaee A(2022)The Sensitivity of Community Extra-Structural Features on Event Prediction in Dynamic Social NetworksSocial Science Computer Review10.1177/0894439321105581341:4(1187-1206)Online publication date: 27-Feb-2022
https://doi.org/10.1177/08944393211055813
Xu XLv WZhang BZhou SWei WLi Y(2021)A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social NetworksComplexity10.1155/2021/88592252021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8859225
Zarayeneh NKalyanaraman A(2021)Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic GraphsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30676658:2(1614-1629)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TNSE.2021.3067665
Show More Cited By

Index Terms

DynamicNet: an effective and efficient algorithm for supporting community evolution detection in time-evolving information networks
1. Information systems
  1. Information storage systems
    1. Storage architectures
      1. Storage network architectures

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cover image ACM Other conferences

IDEAS '13: Proceedings of the 17th International Database Engineering & Applications Symposium

October 2013

222 pages

ISBN:9781450320252

DOI:10.1145/2513591

General Chair:
Bipin C. Desai
Concordia University
,
Program Chairs:
Josep L Larriba-pey
Universitat Politecnica de Catalunya
,
Jorge Bernardino
Instituto Politecnico de Coimbra

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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UPC: Technical University of Catalunya
BytePress
Concordia University: Concordia University

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Association for Computing Machinery

New York, NY, United States

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Published: 09 October 2013

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IDEAS '13

Sponsor:

UPC
Concordia University

IDEAS '13: 17th International Database Engineering & Applications Symposium

October 9 - 13, 2013

Barcelona, Spain

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IDEAS '13 Paper Acceptance Rate 9 of 51 submissions, 18%;

Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

Khafaei TTavakoli Taraghi AHosseinzadeh MRezaee A(2022)The Sensitivity of Community Extra-Structural Features on Event Prediction in Dynamic Social NetworksSocial Science Computer Review10.1177/0894439321105581341:4(1187-1206)Online publication date: 27-Feb-2022
https://doi.org/10.1177/08944393211055813
Xu XLv WZhang BZhou SWei WLi Y(2021)A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social NetworksComplexity10.1155/2021/88592252021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8859225
Zarayeneh NKalyanaraman A(2021)Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic GraphsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30676658:2(1614-1629)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TNSE.2021.3067665
Khafaei TTavakoli Taraghi AHosseinzadeh MRezaee A(2019)Tracing temporal communities and event prediction in dynamic social networksSocial Network Analysis and Mining10.1007/s13278-019-0604-89:1Online publication date: 3-Oct-2019
https://doi.org/10.1007/s13278-019-0604-8
Liu JDu YLi QFu C(2018)Social community evolution by combining gravitational relationship with community structureIntelligent Data Analysis10.3233/IDA-17356122:5(1143-1161)Online publication date: 26-Sep-2018
https://doi.org/10.3233/IDA-173561
Wu JYu LZhang QShi PLiu LDeng SHuang H(2018)Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor DecompositionComplexity10.1155/2018/96534042018(38)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1155/2018/9653404
Atastina ISitohang BSaptawati GMoertini V(2018)An implementation of graph mining to find the group evolution in communication data recordProceedings of the 2018 International Conference on Data Science and Information Technology10.1145/3239283.3239311(79-84)Online publication date: 20-Jul-2018
https://dl.acm.org/doi/10.1145/3239283.3239311
Zhang RJin ZXu PLiu X(2017)A dynamic clustering based method in community detectionCluster Computing10.1007/s10586-017-1472-5Online publication date: 7-Dec-2017
https://doi.org/10.1007/s10586-017-1472-5
Leung CJiang FPoon TCrevier P(2017)Big Data Analytics of Social Network Data: Who Cares Most About You on Facebook?Highlighting the Importance of Big Data Management and Analysis for Various Applications10.1007/978-3-319-60255-4_1(1-15)Online publication date: 23-Aug-2017
https://doi.org/10.1007/978-3-319-60255-4_1
Hu YYang BLv C(2016)A local dynamic method for tracking communities and their evolution in dynamic networksKnowledge-Based Systems10.1016/j.knosys.2016.07.027110:C(176-190)Online publication date: 15-Oct-2016
https://dl.acm.org/doi/10.1016/j.knosys.2016.07.027

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