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Semi-Supervised Deep Learning for Multiplex Networks

Authors:

Priyesh Vijayan,

Ranbir Sanasam,

Diganta Goswami,

Srinivasan Parthasarathy,

Balaraman RavindranAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 1234 - 1244

https://doi.org/10.1145/3447548.3467443

Published: 14 August 2021 Publication History

Abstract

Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer. Such graphs are used to investigate many complex biological, social, and technological systems. In this work, we present a novel semi-supervised approach for structure-aware representation learning on multiplex networks. Our approach relies on maximizing the mutual information between local node-wise patch representations and label correlated structure-aware global graph representations to model the nodes and cluster structures jointly. Specifically, it leverages a novel cluster-aware, node-contextualized global graph summary generation strategy for effective joint-modeling of node and cluster representations across the layers of a multiplex network. Empirically, we demonstrate that the proposed architecture outperforms state-of-the-art methods in a range of tasks: classification, clustering, visualization, and similarity search on seven real-world multiplex networks for various experiment settings.

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Cited By

Gao MJiao PLu RWu HWang YZhao Z(2024)Inductive Link Prediction via Interactive Learning Across Relations in Multiplex NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.317692811:3(3118-3130)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2022.3176928
Chen ZWu ZZhong LPlant CWang SGuo W(2024)Attributed Multi-Order Graph Convolutional Network for Heterogeneous GraphsNeural Networks10.1016/j.neunet.2024.106225174:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106225
Akinnubi AAlassad MAmure RAgarwal N(2024)KG-CFSA: a comprehensive approach for analyzing multi-source heterogeneous social network knowledge graphSocial Network Analysis and Mining10.1007/s13278-024-01320-y14:1Online publication date: 13-Aug-2024
https://doi.org/10.1007/s13278-024-01320-y
Show More Cited By

Index Terms

Semi-Supervised Deep Learning for Multiplex Networks
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Semi-supervised learning settings
    2. Machine learning approaches
      1. Learning latent representations
      2. Neural networks

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Published In

cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

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Published: 14 August 2021

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Gao MJiao PLu RWu HWang YZhao Z(2024)Inductive Link Prediction via Interactive Learning Across Relations in Multiplex NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.317692811:3(3118-3130)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2022.3176928
Chen ZWu ZZhong LPlant CWang SGuo W(2024)Attributed Multi-Order Graph Convolutional Network for Heterogeneous GraphsNeural Networks10.1016/j.neunet.2024.106225174:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106225
Akinnubi AAlassad MAmure RAgarwal N(2024)KG-CFSA: a comprehensive approach for analyzing multi-source heterogeneous social network knowledge graphSocial Network Analysis and Mining10.1007/s13278-024-01320-y14:1Online publication date: 13-Aug-2024
https://doi.org/10.1007/s13278-024-01320-y
Akinnubi AAlassad MAgarwal NAmure R(2024)Identifying Contextualized Focal Structures in Multisource Social Networks by Leveraging Knowledge GraphsComplex Networks & Their Applications XII10.1007/978-3-031-53472-0_2(15-27)Online publication date: 21-Feb-2024
https://doi.org/10.1007/978-3-031-53472-0_2
Qiu CGeng YLu JChen KZhu SSu YNan GZhang CFu JCui QTao XSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599238(1965-1977)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599238
Abdous KMrabah NBouguessa M(2023)Hierarchical Aggregations for High-Dimensional Multiplex Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330580936:4(1624-1637)Online publication date: 16-Aug-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3305809
Taha K(2023)Semi-supervised and un-supervised clusteringInformation Systems10.1016/j.is.2023.102178114:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102178
Mitra AVijayan PSingh SGoswami DParthasarathy SRavindran B(2022)Revisiting Link Prediction on Heterogeneous Graphs with a Multi-view Perspective2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00046(358-367)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00046
Alassad MAgarwal N(2022)Contextualizing focal structure analysis in social networksSocial Network Analysis and Mining10.1007/s13278-022-00938-012:1Online publication date: 8-Aug-2022
https://doi.org/10.1007/s13278-022-00938-0

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