research-article

Representation Learning of Enhanced Graphs Using Random Walk Graph Convolutional Network

Authors:

Xiangnan FengAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 3

Article No.: 46, Pages 1 - 21

https://doi.org/10.1145/3582841

Published: 24 March 2023 Publication History

Abstract

Nowadays, graph structure data has played a key role in machine learning because of its simple topological structure, and therefore, the graph representation learning methods have attracted great attention. And it turns out that the low-dimensional embedding representation obtained by graph representation learning is extremely useful in various typical tasks, such as node classification and content recommendation. However, most of the existing methods do not further dig out potential structural information on the original graph structure. Here, we propose wGCN, which utilizes random walk to obtain the node-specific mesoscopic structures (high-order local structure) of the graph and utilizes these mesoscopic structures to enhance the graph and organize the characteristic information of the nodes. Our method can effectively generate node embedding for data of previously unknown categories, which has been proven in a series of experiments conducted on many types of graph networks. And compared to baselines, our method shows the best performance on most datasets and achieves competitive results on others. It is believed that combining the mesoscopic structure to further explore the structural information of the graph will greatly improve the learning efficiency of the graph neural network.

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Cited By

Kang JZhang JChen LZhang HZhuo L(2025)RWGCN: Random walk graph convolutional network for group activity recognitionApplied Intelligence10.1007/s10489-024-06017-555:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-024-06017-5
Deng MZhang WZhao JWang ZZhou MLuo JChen C(2024)A Novel Framework for Joint Learning of City Region Partition and RepresentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365285720:7(1-23)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3652857
Hao MCai MFang MYou L(2024)SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation SystemsACM Transactions on Intelligent Systems and Technology10.1145/364386115:2(1-20)Online publication date: 28-Mar-2024
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Index Terms

Representation Learning of Enhanced Graphs Using Random Walk Graph Convolutional Network
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 3

June 2023

451 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3587032

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2023

Online AM: 10 February 2023

Accepted: 23 January 2023

Revised: 06 November 2022

Received: 26 November 2021

Published in TIST Volume 14, Issue 3

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National Natural Science Foundation of China
Beijing Natural Science Foundation
Fundamental Research Funds for the Central Universities

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Cited By

Kang JZhang JChen LZhang HZhuo L(2025)RWGCN: Random walk graph convolutional network for group activity recognitionApplied Intelligence10.1007/s10489-024-06017-555:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-024-06017-5
Deng MZhang WZhao JWang ZZhou MLuo JChen C(2024)A Novel Framework for Joint Learning of City Region Partition and RepresentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365285720:7(1-23)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3652857
Hao MCai MFang MYou L(2024)SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation SystemsACM Transactions on Intelligent Systems and Technology10.1145/364386115:2(1-20)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3643861
Yu YSugiyama KJatowt AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation with Collaborative Explanation via Mutual Information MaximizationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657770(1062-1072)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657770
Zhang YSun YZhuang FZhu YAn ZXu Y(2023)Triple Dual Learning for Opinion-based Explainable RecommendationACM Transactions on Information Systems10.1145/363152142:3(1-27)Online publication date: 30-Dec-2023
https://dl.acm.org/doi/10.1145/3631521
Yu DWang XXiong YShen XWu RWang DZou ZXu G(2023)MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online GamesACM Transactions on Intelligent Systems and Technology10.1145/3626243Online publication date: 9-Oct-2023
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Li THui SZhang SWang HZhang YHui PJin DLi Y(undefined)Mobile User Traffic Generation via Multi-Scale Hierarchical GANACM Transactions on Knowledge Discovery from Data10.1145/3664655
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