HITS-GNN: A Simplified Propagation Scheme for Graph Neural Networks

Khan, Mehak M.; Mello, Gustavo Borges Moreno e; Engelstad, Paal E.; Habib, Laurence; Yazidi, Anis

doi:10.1109/bigdata55660.2022.10020815

2022 IEEE International Conference on Big Data (Big Data) 2022

DOI: 10.1109/bigdata55660.2022.10020815

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HITS-GNN: A Simplified Propagation Scheme for Graph Neural Networks

Mehak M. Khan

Gustavo Borges Moreno e Mello

Paal E. Engelstad

et al.

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2024

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HITS-based Propagation Paradigm for Graph Neural Networks

Khan,

Mello,

Habib

et al. 2024

ACM Trans. Knowl. Discov. Data

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In this paper, we present a new propagation paradigm based on the principle of Hyperlink-Induced Topic Search (HITS) algorithm. The HITS algorithm utilizes the concept of a ”self-reinforcing” relationship of authority-hub. Using HITS, the centrality of nodes is determined via repeated updates of authority-hub scores that converge to a stationary distribution. Unlike PageRank-based propagation methods, which rely solely on the idea of authorities (in-links), HITS considers the relevance of both authorities (in-links) and hubs (out-links), thereby allowing for a more informative graph learning process. To segregate node prediction and propagation, we use a Multilayer Perceptron (MLP) in combination with a HITS-based propagation approach and propose two models; HITS-GNN and HITS-GNN+. We provided additional validation of our models’ efficacy by performing an ablation study to assess the performance of authority-hub in independent models. Moreover, the effect of the main hyper-parameters and normalization is also analyzed to uncover how these techniques influence the performance of our models. Extensive experimental results indicate that the proposed approach significantly improves baseline methods on the graph (citation network) benchmark datasets by a decent margin for semi-supervised node classification, which can aid in predicting the categories (labels) of scientific articles not exclusively based on their content but also based on the type of articles they cite.

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HITS-based Propagation Paradigm for Graph Neural Networks

Khan,

Mello,

Habib

et al. 2024

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

show abstract

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HITS-GNN: A Simplified Propagation Scheme for Graph Neural Networks

Cited by 1 publication

References 23 publications

HITS-based Propagation Paradigm for Graph Neural Networks

HITS-based Propagation Paradigm for Graph Neural Networks

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