research-article

Constant-factor approximation algorithms for identifying dynamic communities

Authors:

Chayant Tantipathananandh,

Tanya Berger-WolfAuthors Info & Claims

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 827 - 836

https://doi.org/10.1145/1557019.1557110

Published: 28 June 2009 Publication History

Abstract

We propose two approximation algorithms for identifying communities in dynamic social networks. Communities are intuitively characterized as "unusually densely knit" subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Recently, we have proposed an optimization-based framework for modeling dynamic community structure. Also, we have proposed an algorithm for finding such structure based on maximum weight bipartite matching. In this paper, we analyze its performance guarantee for a special case where all actors can be observed at all times. In such instances, we show that the algorithm is a small constant factor approximation of the optimum. We use a similar idea to design an approximation algorithm for the general case where some individuals are possibly unobserved at times, and to show that the approximation factor increases twofold but remains a constant regardless of the input size. This is the first algorithm for inferring communities in dynamic networks with a provable approximation guarantee. We demonstrate the general algorithm on real data sets. The results confirm the efficiency and effectiveness of the algorithm in identifying dynamic communities.

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https://doi.org/10.1016/j.knosys.2023.111163
Kong CChen BZhang L(2020)DEM: Deep Entity Matching Across Heterogeneous Information NetworksJournal of Computer Science and Technology10.1007/s11390-020-0139-535:4(739-750)Online publication date: 27-Jul-2020
https://doi.org/10.1007/s11390-020-0139-5
Atzmueller M(2019)Onto Model-based Anomalous Link Pattern Mining on Feature-Rich Social Interaction NetworksCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316707(1047-1050)Online publication date: 13-May-2019
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Index Terms

Constant-factor approximation algorithms for identifying dynamic communities
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

June 2009

1426 pages

ISBN:9781605584959

DOI:10.1145/1557019

General Chairs:
John Elder
Elder Research, Inc., USA
,
Françoise Soulié Fogelman
KXEN, France
,
Program Chairs:
Peter Flach
University of Bristol, UK
,
Mohammed Zaki
RPI, USA

Copyright © 2009 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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Published: 28 June 2009

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June 28 - July 1, 2009

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.1016/j.knosys.2023.111163
Kong CChen BZhang L(2020)DEM: Deep Entity Matching Across Heterogeneous Information NetworksJournal of Computer Science and Technology10.1007/s11390-020-0139-535:4(739-750)Online publication date: 27-Jul-2020
https://doi.org/10.1007/s11390-020-0139-5
Atzmueller M(2019)Onto Model-based Anomalous Link Pattern Mining on Feature-Rich Social Interaction NetworksCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316707(1047-1050)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316707
Kong CGao MXu CFu YQian WZhou A(2019)EnAliFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6561-313:1(157-169)Online publication date: 1-Feb-2019
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Xu GWu ZCao JTao H(2018)Models for Community DynamicsEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_180(1378-1392)Online publication date: 12-Jun-2018
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Xu GWu ZCao JTao H(2017)Models for Community DynamicsEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_180-1(1-15)Online publication date: 28-Aug-2017
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Abdelsadek YChelghoum KHerrmann FKacem IOtjacques B(2016)On the Community Identification in Weighted Time-Varying NetworksSwarm Intelligence Based Optimization10.1007/978-3-319-50307-3_9(111-123)Online publication date: 25-Nov-2016
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