short-paper

TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks

Authors:

Yeon-Chang Lee,

Sang-Wook KimAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2451 - 2455

https://doi.org/10.1145/3539618.3592075

Published: 18 July 2023 Publication History

Abstract

The problem of signed network embedding (SNE) aims to represent nodes in a given signed network as low-dimensional vectors. While several SNE methods based on graph convolutional networks (GCN) have been proposed, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which measures the trustworthiness on edge signs for high-order relationships inferred by balance theory and corrects incorrect embedding propagation based on the trustworthiness. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.

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https://doi.org/10.1186/s12915-024-01968-0
Minici MCinus FBonchi FManco GSerra ESpezzano F(2024)Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social BalanceProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679786(1689-1699)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679786
Kim MLee YKim SSerra ESpezzano F(2024)PolarDSN: An Inductive Approach to Learning the Evolution of Network Polarization in Dynamic Signed NetworksProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679654(1099-1109)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679654
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Index Terms

TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks
1. Information systems
  1. Information systems applications

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Information

Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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Publication History

Published: 18 July 2023

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National Research Foundation of Korea(NRF)
Institute of Information & Communications Technology Planning & Evaluation (IITP)

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SIGIR '23

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Cited By

Zhu HHao HYu L(2024)Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagationBMC Biology10.1186/s12915-024-01968-022:1Online publication date: 15-Aug-2024
https://doi.org/10.1186/s12915-024-01968-0
Minici MCinus FBonchi FManco GSerra ESpezzano F(2024)Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social BalanceProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679786(1689-1699)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679786
Kim MLee YKim SSerra ESpezzano F(2024)PolarDSN: An Inductive Approach to Learning the Evolution of Network Polarization in Dynamic Signed NetworksProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679654(1099-1109)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679654
Sharma KRaghavendra MLee YM AKumar SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Representation Learning in Continuous-Time Dynamic Signed NetworksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615032(2229-2238)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615032

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