Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs

Shouheng Li*, Dongwoo Kim, Qing Wang

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    11 Citations (Scopus)

    Abstract

    Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs. As pointed out by recent studies, most GNNs assume local homophily, i.e., strong similarities in local neighborhoods. This assumption however limits the generalizability power of GNNs. To address this limitation, we propose a flexible GNN model, which is capable of handling any graphs without being restricted by their underlying homophily. At its core, this model adopts a node attention mechanism based on multiple learnable spectral filters; therefore, the aggregation scheme is learned adaptively for each graph in the spectral domain. We evaluated the proposed model on node classification tasks over eight benchmark datasets. The proposed model is shown to generalize well to both homophilic and heterophilic graphs. Further, it outperforms all state-of-the-art baselines on heterophilic graphs and performs comparably with them on homophilic graphs.

    Original languageEnglish
    Title of host publicationMachine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Proceedings
    EditorsNuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, Jose A. Lozano
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages450-465
    Number of pages16
    ISBN (Print)9783030865191
    DOIs
    Publication statusPublished - 2021
    EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
    Duration: 13 Sept 202117 Sept 2021

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12976 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
    CityVirtual, Online
    Period13/09/2117/09/21

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