research-article

NEAR: Neighborhood Edge AggregatoR for Graph Classification

Authors:

Cheolhyeong Kim,

Hyung Ju HwangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 3

Article No.: 45, Pages 1 - 17

https://doi.org/10.1145/3506714

Published: 22 April 2022 Publication History

Abstract

Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformatics, social network analysis, and data mining. Recent GNN algorithms are based on neural message passing, which enables GNNs to integrate local structures and node features recursively. However, past GNN algorithms based on 1-hop neighborhood neural message passing are exposed to a risk of loss of information on local structures and relationships. In this article, we propose Neighborhood Edge AggregatoR (NEAR), a framework that aggregates relations between the nodes in the neighborhood via edges. NEAR, which can be orthogonally combined with Graph Isomorphism Network (GIN), gives integrated information that describes which nodes in the neighborhood are connected. Therefore, NEAR can reflect additional information of a local structure of each node beyond the nodes themselves in 1-hop neighborhood. Experimental results on multiple graph classification tasks show that our algorithm makes a good improvement over other existing 1-hop based GNN-based algorithms.

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Cited By

Nagaraj CAnand MPriyadharshini SAparna P(2024)GCNXG: Detecting Fraudulent Activities in Financial Networks: A Graph Analytics and Machine Learning FusionRenewable Energy, Green Computing, and Sustainable Development10.1007/978-3-031-58607-1_2(17-32)Online publication date: 18-Apr-2024
https://doi.org/10.1007/978-3-031-58607-1_2

Index Terms

NEAR: Neighborhood Edge AggregatoR for Graph Classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks
2. General and reference
  1. Document types
    1. General literature

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 3

June 2022

415 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3508465

Editor:
Huan Liu
Arizona State University, USA

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Association for Computing Machinery

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Publication History

Published: 22 April 2022

Accepted: 01 December 2021

Revised: 01 June 2021

Received: 01 July 2020

Published in TIST Volume 13, Issue 3

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Basic Science Research Program through the National Research Foundation of Korea
Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP)
Artificial Intelligence Graduate School Program (POSTECH)
ITRC (Information Technology Research Center)

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Cited By

Nagaraj CAnand MPriyadharshini SAparna P(2024)GCNXG: Detecting Fraudulent Activities in Financial Networks: A Graph Analytics and Machine Learning FusionRenewable Energy, Green Computing, and Sustainable Development10.1007/978-3-031-58607-1_2(17-32)Online publication date: 18-Apr-2024
https://doi.org/10.1007/978-3-031-58607-1_2

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