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Network Analysis with Negative Links

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Tyler DerrAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 917 - 918

https://doi.org/10.1145/3336191.3372188

Published: 22 January 2020 Publication History

Abstract

As we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively/efficiently extract insightful patterns. Then, once paired with domain knowledge, we can seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis. Furthermore, many real-world networks can be better represented as signed networks, e.g., in an online social network such as Facebook, friendships can be represented as positive links while negative links can represent blocked users. Hence, due to signed networks being ubiquitous, in this work we seek to provide a fundamental background into the domain, a hierarchical categorization of existing work highlighting both seminal and state of the art, provide a curated collection of signed network datasets, and discuss important future directions.

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Digital Library

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Tyler Derr, Cassidy Johnson, Yi Chang, and Jiliang Tang. 2019. Balance in Signed Bipartite Networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM, 1221--1230.

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Cited By

Chen JDai FGu XFan HZhou JLi BWang WFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Learning Pair-Centric Representation for Link Sign Prediction with SubgraphProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614951(256-265)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614951
Abbasi FMuzammal M(2022)Exploiting Modularity Maximisation in Signed Network Communities for Link PredictionProceedings of International Conference on Information Technology and Applications10.1007/978-981-16-7618-5_36(417-427)Online publication date: 21-Apr-2022
https://doi.org/10.1007/978-981-16-7618-5_36
Shu LDu EChang YChen CZheng ZXing XShen SDemartini GZuccon GCulpepper JHuang ZTong H(2021)SGCLProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482478(1671-1680)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482478
Show More Cited By

Index Terms

Network Analysis with Negative Links
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Discrete mathematics

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
,
Xia "Ben" Hu
Texas A&M University
,
Program Chairs:
Mounia Lalmas
Spotify
,
Wei Wang
University of California, Los Angeles

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 22 January 2020

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National Science Foundation

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WSDM '20

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WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining

February 3 - 7, 2020

TX, Houston, USA

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Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Chen JDai FGu XFan HZhou JLi BWang WFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Learning Pair-Centric Representation for Link Sign Prediction with SubgraphProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614951(256-265)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614951
Abbasi FMuzammal M(2022)Exploiting Modularity Maximisation in Signed Network Communities for Link PredictionProceedings of International Conference on Information Technology and Applications10.1007/978-981-16-7618-5_36(417-427)Online publication date: 21-Apr-2022
https://doi.org/10.1007/978-981-16-7618-5_36
Shu LDu EChang YChen CZheng ZXing XShen SDemartini GZuccon GCulpepper JHuang ZTong H(2021)SGCLProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482478(1671-1680)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482478
Javari ADerr TEsmailian PTang JChang K(2020)ROSE: Role-based Signed Network EmbeddingProceedings of The Web Conference 202010.1145/3366423.3380038(2782-2788)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380038
Derr TWang ZDacon JTang J(2020)Link and interaction polarity predictions in signed networksSocial Network Analysis and Mining10.1007/s13278-020-0630-610:1Online publication date: 10-Mar-2020
https://doi.org/10.1007/s13278-020-0630-6

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