research-article

Discovering missing links in networks using vertex similarity measures

Authors:

Hung-Hsuan Chen,

Xiaolong (Luke) Zhang,

C. Lee GilesAuthors Info & Claims

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

Pages 138 - 143

https://doi.org/10.1145/2245276.2245305

Published: 26 March 2012 Publication History

Abstract

Vertex similarity measure is a useful tool to discover the hidden relationships of vertices in a complex network. We introduce relation strength similarity (RSS), a vertex similarity measure that could better capture potential relationships of real world network structure. RSS is unique in that is is an asymmetric measure which could be used for a more general purpose social network analysis; allows users to explicitly specify the relation strength between neighboring vertices for initialization; and offers a discovery range parameter could be adjusted by users for extended network degree search. To show the potential of vertex similarity measures and the superiority of RSS over other measures, we conduct experiments on two real networks, a biological network and a coauthorship network. Experimental results show that RSS is better in discovering the hidden relationships of the networks.

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Cited By

Yang JWu Y(2024)Link prediction based on depth structure in social networksInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02178-415:10(4639-4657)Online publication date: 14-May-2024
https://doi.org/10.1007/s13042-024-02178-4
Wang HLiu GHu P(2023)TDAN: Transferable Domain Adversarial Network for Link Prediction in Heterogeneous Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/361022918:1(1-22)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3610229
Wang RChen H(2023)Detecting Inactive Cyberwarriors from Online Forums2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00008(9-15)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00008
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Index Terms

Discovering missing links in networks using vertex similarity measures
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Conferences

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

March 2012

2179 pages

ISBN:9781450308571

DOI:10.1145/2245276

Conference Chairs:
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Paola Lecca
The Microsoft Research - University of Trento COSBI, Italy

Copyright © 2012 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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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 26 March 2012

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SAC 2012

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SAC 2012: ACM Symposium on Applied Computing

March 26 - 30, 2012

Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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https://doi.org/10.1007/s13042-024-02178-4
Wang HLiu GHu P(2023)TDAN: Transferable Domain Adversarial Network for Link Prediction in Heterogeneous Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/361022918:1(1-22)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3610229
Wang RChen H(2023)Detecting Inactive Cyberwarriors from Online Forums2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00008(9-15)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00008
Yang JWu Y(2023)A link prediction method based on compressed sensing for social networksApplied Intelligence10.1007/s10489-023-05060-y53:23(29300-29318)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-05060-y
Wang PWu CHuang TChen Y(2022)A Supervised Link Prediction Method Using Optimized Vertex Collocation ProfileEntropy10.3390/e2410146524:10(1465)Online publication date: 14-Oct-2022
https://doi.org/10.3390/e24101465
Yazdani MMoeini AMazoochi MRahmani FRabiei L(2022)On the reduction of instability of label propagation algorithmInternational Journal of Modern Physics C10.1142/S012918312350053534:04Online publication date: 25-Oct-2022
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Choi JTosyali AKim BLee HJeong M(2022)A Novel Method for Identifying Competitors Using a Financial Transaction NetworkIEEE Transactions on Engineering Management10.1109/TEM.2019.293166069:4(845-860)Online publication date: Aug-2022
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Mackay SPonce COsborn SMcGarry M(2021)Finding diverse ways to improve algebraic connectivity through multi-start optimizationJournal of Complex Networks10.1093/comnet/cnab0059:1Online publication date: 25-Apr-2021
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Zou LWang CZeng AFan YDi Z(2021)Link prediction in growing networks with agingSocial Networks10.1016/j.socnet.2020.11.00165(1-7)Online publication date: May-2021
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