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Multi-Dimensional Network Embedding with Hierarchical Structure

Authors:

Dawei YinAuthors Info & Claims

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Pages 387 - 395

https://doi.org/10.1145/3159652.3159680

Published: 02 February 2018 Publication History

Abstract

Information networks are ubiquitous in many applications. A popular way to facilitate the information in a network is to embed the network structure into low-dimension spaces where each node is represented as a vector. The learned representations have been proven to advance various network analysis tasks such as link prediction and node classification. The majority of existing embedding algorithms are designed for the networks with one type of nodes and one dimension of relations among nodes. However, many networks in the real-world complex systems have multiple types of nodes and multiple dimensions of relations. For example, an e-commerce network can have users and items, and items can be viewed or purchased by users, corresponding to two dimensions of relations. In addition, some types of nodes can present hierarchical structure. For example, authors in publication networks are associated to affiliations; and items in e-commerce networks belong to categories. Most of existing methods cannot be naturally applicable to these networks. In this paper, we aim to learn representations for networks with multiple dimensions and hierarchical structure. In particular, we provide an approach to capture independent information from each dimension and dependent information across dimensions and propose a framework MINES, which performs Multi-dImension Network Embedding with hierarchical Structure. Experimental results on a network from a real-world e-commerce website demonstrate the effectiveness of the proposed framework.

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Multi-Dimensional Network Embedding with Hierarchical Structure
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cover image ACM Conferences

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

February 2018

821 pages

ISBN:9781450355810

DOI:10.1145/3159652

General Chairs:
Yi Chang
Jilin University, Huawei Inc.
,
Chengxiang Zhai
University of Illinois Urbana-Champaign
,
Program Chairs:
Yan Liu
University of Southern California
,
Yoelle Maarek
Amazon

Copyright © 2018 ACM.

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Published: 02 February 2018

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The National Science Foundation

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WSDM 2018

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WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining

February 5 - 9, 2018

CA, Marina Del Rey, USA

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WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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https://doi.org/10.1109/TKDE.2021.3139035
Zhao AYu Y(2023)Multi-view computable online learner modeling by heterogeneous network: An AI-enabled perspectiveInformation Sciences10.1016/j.ins.2023.119363645(119363)Online publication date: Oct-2023
https://doi.org/10.1016/j.ins.2023.119363
Gao JAl-Sabri ROloulade BChen JLyu TWu Z(2023)GM2NAS: multitask multiview graph neural architecture searchKnowledge and Information Systems10.1007/s10115-023-01886-765:10(4021-4054)Online publication date: 15-May-2023
https://doi.org/10.1007/s10115-023-01886-7
Xie ZWu MZhao GZhou LGong ZZhang Z(2023)Attributed Multi-relational Graph Embedding Based on GCNAdvanced Intelligent Computing Technology and Applications10.1007/978-981-99-4742-3_14(174-186)Online publication date: 30-Jul-2023
https://doi.org/10.1007/978-981-99-4742-3_14
Bai NNourian PLuo RPereira Roders A(2022)Heri-Graphs: A Dataset Creation Framework for Multi-Modal Machine Learning on Graphs of Heritage Values and Attributes with Social MediaISPRS International Journal of Geo-Information10.3390/ijgi1109046911:9(469)Online publication date: 30-Aug-2022
https://doi.org/10.3390/ijgi11090469
Xie LHuang HDu Q(2022)A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social NetworksApplied Sciences10.3390/app1203132112:3(1321)Online publication date: 26-Jan-2022
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Zhou ZWang THou LZhou XMa MDing ZAl Hasan MXiong L(2022)Decoupled Hyperbolic Graph Attention Network for Modeling Substitutable and Complementary Item RelationshipsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557281(2763-2772)Online publication date: 17-Oct-2022
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Lin LBlaser EWang HSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Graph Embedding with Hierarchical Attentive MembershipProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498499(582-590)Online publication date: 11-Feb-2022
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Li BJing BTong H(2022)Graph Communal Contrastive LearningProceedings of the ACM Web Conference 202210.1145/3485447.3512208(1203-1213)Online publication date: 25-Apr-2022
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Huang HSong YWu YShi JXie XJin H(2022)Multitask Representation Learning With Multiview Graph Convolutional NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.303682533:3(983-995)Online publication date: Mar-2022
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