research-article

The Troll-Trust Model for Ranking in Signed Networks

Authors:

Charu C. Aggarwal,

Jimeng SunAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 447 - 456

https://doi.org/10.1145/2835776.2835816

Published: 08 February 2016 Publication History

Abstract

Signed social networks have become increasingly important in recent years because of the ability to model trust-based relationships in review sites like Slashdot, Epinions, and Wikipedia. As a result, many traditional network mining problems have been re-visited in the context of networks in which signs are associated with the links. Examples of such problems include community detection, link prediction, and low rank approximation. In this paper, we will examine the problem of ranking nodes in signed networks. In particular, we will design a ranking model, which has a clear physical interpretation in terms of the sign of the edges in the network. Specifically, we propose the Troll-Trust model that models the probability of trustworthiness of individual data sources as an interpretation for the underlying ranking values. We will show the advantages of this approach over a variety of baselines.

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Cited By

Pan YJi XYou JLi LLiu ZZhang XZhang ZWang M(2025)CSGDN: contrastive signed graph diffusion network for predicting crop gene–phenotype associationsBriefings in Bioinformatics10.1093/bib/bbaf06226:1Online publication date: 20-Feb-2025
https://doi.org/10.1093/bib/bbaf062
Bu YZhu YGeng LZhou K(2025)Unleashing the power of indirect attacks against trust prediction via preferential pathKnowledge and Information Systems10.1007/s10115-024-02327-9Online publication date: 6-Feb-2025
https://doi.org/10.1007/s10115-024-02327-9
Lin WLi B(2024)Status-Aware Signed Heterogeneous Network Embedding With Graph Neural NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.3151046(1-13)Online publication date: 2024
https://doi.org/10.1109/TNNLS.2022.3151046
Show More Cited By

Index Terms

The Troll-Trust Model for Ranking in Signed Networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 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: 08 February 2016

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U.S. Army Research Laboratory

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WSDM 2016

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

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

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Citations

Cited By

Pan YJi XYou JLi LLiu ZZhang XZhang ZWang M(2025)CSGDN: contrastive signed graph diffusion network for predicting crop gene–phenotype associationsBriefings in Bioinformatics10.1093/bib/bbaf06226:1Online publication date: 20-Feb-2025
https://doi.org/10.1093/bib/bbaf062
Bu YZhu YGeng LZhou K(2025)Unleashing the power of indirect attacks against trust prediction via preferential pathKnowledge and Information Systems10.1007/s10115-024-02327-9Online publication date: 6-Feb-2025
https://doi.org/10.1007/s10115-024-02327-9
Lin WLi B(2024)Status-Aware Signed Heterogeneous Network Embedding With Graph Neural NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.3151046(1-13)Online publication date: 2024
https://doi.org/10.1109/TNNLS.2022.3151046
Zhou AWang YChen LÖzsu M(2024)Positive Communities on Signed Graphs That Are Not Echo Chambers: A Clique-Based Approach2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00199(2531-2543)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00199
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
Chakraborty RDas RChandra J(2023)SigGAN: Adversarial Model for Learning Signed Relationships in NetworksACM Transactions on Knowledge Discovery from Data10.1145/353261017:1(1-20)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3532610
Zhang ZZhou MWan JLu KChen GLiao H(2023)Spammer detection via ranking aggregation of group behaviorExpert Systems with Applications10.1016/j.eswa.2022.119454216(119454)Online publication date: Apr-2023
https://doi.org/10.1016/j.eswa.2022.119454
Tang SWong R(2023)Unsupervised Fraud Detection on Sparse Rating NetworksData Science and Machine Learning10.1007/978-981-99-8696-5_2(19-33)Online publication date: 5-Dec-2023
https://doi.org/10.1007/978-981-99-8696-5_2
Jung JYoo JKang U(2022)Signed random walk diffusion for effective representation learning in signed graphsPLOS ONE10.1371/journal.pone.026500117:3(e0265001)Online publication date: 17-Mar-2022
https://doi.org/10.1371/journal.pone.0265001
Das D(2022)A Novel Negative Link Prediction Algorithm for Social Networks2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)10.1109/VTC2022-Spring54318.2022.9860751(1-5)Online publication date: Jun-2022
https://doi.org/10.1109/VTC2022-Spring54318.2022.9860751
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