research-article

Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification

Authors:

Chong-Kwon KimAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 283 - 292

https://doi.org/10.1145/3511808.3557324

Published: 17 October 2022 Publication History

Abstract

Graph Neural Networks (GNNs) have proven to be powerful in many graph-based applications. However, they fail to generalize well under heterophilic setups, where neighbor nodes have different labels. To address this challenge, we employ a confidence ratio as a hyper-parameter, assuming that some of the edges are disassortative (heterophilic). Here, we propose a two-phased algorithm. Firstly, we determine edge coefficients through subgraph matching using a supplementary module. Then, we apply GNNs with a modified label propagation mechanism to utilize the edge coefficients effectively. Specifically, our supplementary module identifies a certain proportion of task-irrelevant edges based on a given confidence ratio. Using the remaining edges, we employ the widely used optimal transport to measure the similarity between two nodes with their subgraphs. Finally, using the coefficients as supplementary information on GNNs, we improve the label propagation mechanism which can prevent two nodes with smaller weights from being closer. The experiments on benchmark datasets show that our model alleviates over-smoothing and improves performance.

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Choi SKim WKim SIn YKim SPark CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645561(733-744)Online publication date: 13-May-2024
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Index Terms

Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

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Published: 17 October 2022

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October 17 - 21, 2022

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

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Cited By

Zheng ZZhou SXu HGu MXu YLi ALi YGu JBu J(2025)Heterophilous distribution propagation for Graph Neural NetworksNeural Networks10.1016/j.neunet.2024.107014184(107014)Online publication date: Apr-2025
https://doi.org/10.1016/j.neunet.2024.107014
Wu JLiu XCheng DOuyang YWu XZheng YLarson K(2024)Safeguarding fraud detection from attacksProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/830(7500-7508)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/830
Choi SKim WKim SIn YKim SPark CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645561(733-744)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645561
In YYoon KKim KShin KPark CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Self-Guided Robust Graph Structure RefinementProceedings of the ACM Web Conference 202410.1145/3589334.3645522(697-708)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645522
Choi YShah RSabo JLiu HCandan K(2024)Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825821(1956-1963)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825821
Chen GWang YGuo FGuo QShao JShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Causality and Independence Enhancement for Biased Node ClassificationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614804(203-212)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614804

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