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Signed Network Modeling Based on Structural Balance Theory

Authors:

Charu Aggarwal,

Jiliang TangAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 557 - 566

https://doi.org/10.1145/3269206.3271746

Published: 17 October 2018 Publication History

Abstract

The modeling of networks, specifically generative models, has been shown to provide a plethora of information about the underlying network structures, as well as many other benefits behind their construction. There has been a considerable increase in interest for the better understanding and modeling of networks, and the vast majority of existing work has been for unsigned networks. However, many networks can have positive and negative links (or signed networks), especially in online social media. It is evident from recent work that signed networks present unique properties and principles from unsigned networks due to the added complexity, which pose tremendous challenges on existing unsigned network models. Hence, in this paper, we investigate the problem of modeling signed networks. In particular, we provide a principled approach to capture important properties and principles of signed networks and propose a novel signed network model guided by Structural Balance Theory. Empirical experiments on three real-world signed networks demonstrate the effectiveness of the proposed model.

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Cited By

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https://doi.org/10.26599/BDMA.2024.9020033
Arya ASerra ESpezzano F(2024)Realistic Synthetic Signed Network Generation and AnalysisProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680274(5439-5442)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680274
Arya APandey P(2024)SISSRM: Sequentially Induced Signed Subnetwork Reconstruction Model for Generating Realistic Synthetic Signed NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339261311:5(6476-6486)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3392613
Show More Cited By

Index Terms

Signed Network Modeling Based on Structural Balance Theory
1. Information systems
  1. Information systems applications
    1. Data mining
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Random network models

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Published: 17 October 2018

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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Abbasi FMuzammal MQu QRiaz FAshraf J(2024)SNCA: Semi-Supervised Node Classification for Evolving Large Attributed GraphsBig Data Mining and Analytics10.26599/BDMA.2024.90200337:3(794-808)Online publication date: Sep-2024
https://doi.org/10.26599/BDMA.2024.9020033
Arya ASerra ESpezzano F(2024)Realistic Synthetic Signed Network Generation and AnalysisProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680274(5439-5442)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680274
Arya APandey P(2024)SISSRM: Sequentially Induced Signed Subnetwork Reconstruction Model for Generating Realistic Synthetic Signed NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339261311:5(6476-6486)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3392613
Tang WZhu J(2024)Population-Level Balance in Signed NetworksJournal of the American Statistical Association10.1080/01621459.2024.2356894(1-13)Online publication date: 26-Jun-2024
https://doi.org/10.1080/01621459.2024.2356894
Gallo AGarlaschelli DLambiotte RSaracco FSquartini T(2024)Testing structural balance theories in heterogeneous signed networksCommunications Physics10.1038/s42005-024-01640-77:1Online publication date: 13-May-2024
https://doi.org/10.1038/s42005-024-01640-7
Gullo FMandaglio DTagarelli A(2024)Neural discovery of balance-aware polarized communitiesMachine Learning10.1007/s10994-024-06581-4Online publication date: 9-Jul-2024
https://doi.org/10.1007/s10994-024-06581-4
Song AJi RQi WZhang C(2023)RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network ClusteringApplied Sciences10.3390/app1303136713:3(1367)Online publication date: 19-Jan-2023
https://doi.org/10.3390/app13031367
Chung KZhou AWang YChen L(2023)Maximum Balanced (k,ϵ)-Bitruss Detection in Signed Bipartite GraphProceedings of the VLDB Endowment10.14778/3632093.363209917:3(332-344)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.14778/3632093.3632099
Yang FMa HYan CLi ZChang L(2023)Polarized Communities Search via Co-guided Random Walk in Attributed Signed NetworksACM Transactions on Internet Technology10.1145/361344923:4(1-22)Online publication date: 17-Nov-2023
https://dl.acm.org/doi/10.1145/3613449
Arya APandey PSaxena A(2023)Balanced and Unbalanced Triangle Count in Signed NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327265735:12(12491-12496)Online publication date: 1-Dec-2023
https://doi.org/10.1109/TKDE.2023.3272657
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