research-article

A Normalizing Flow-Based Co-Embedding Model for Attributed Networks

Authors:

Shangsong Liang,

Zaiqiao MengAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 3

Article No.: 55, Pages 1 - 31

https://doi.org/10.1145/3477049

Published: 22 October 2021 Publication History

Abstract

Network embedding is a technique that aims at inferring the low-dimensional representations of nodes in a semantic space. In this article, we study the problem of inferring the low-dimensional representations of both nodes and attributes for attributed networks in the same semantic space such that the affinity between a node and an attribute can be effectively measured. Intuitively, this problem can be addressed by simply utilizing existing variational auto-encoder (VAE) based network embedding algorithms. However, the variational posterior distribution in previous VAE based network embedding algorithms is often assumed and restricted to be a mean-field Gaussian distribution or other simple distribution families, which results in poor inference of the embeddings. To alleviate the above defect, we propose a novel VAE-based co-embedding method for attributed network, F-CAN, where posterior distributions are flexible, complex, and scalable distributions constructed through the normalizing flow. We evaluate our proposed models on a number of network tasks with several benchmark datasets. Experimental results demonstrate that there are clear improvements in the qualities of embeddings generated by our model to the state-of-the-art attributed network embedding methods.

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Cited By

Liu AHuang YWu YLiu SLiang S(2024)Active Learning for Multi-Class Drug-Drug Interactions Prediction2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822197(1611-1615)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822197
Mo XWan BTang RDing JLiu G(2023)Attention‐based network embedding with higher‐order weights and node attributesCAAI Transactions on Intelligence Technology10.1049/cit2.12215Online publication date: 5-Apr-2023
https://doi.org/10.1049/cit2.12215
Chen LLiang S(2022)Cross-Temporal Snapshot Alignment for Dynamic Multi-Relational NetworksJournal of Physics: Conference Series10.1088/1742-6596/2253/1/0120382253:1(012038)Online publication date: 1-Apr-2022
https://doi.org/10.1088/1742-6596/2253/1/012038
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Index Terms

A Normalizing Flow-Based Co-Embedding Model for Attributed Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 3

June 2022

494 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3485152

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 22 October 2021

Accepted: 01 July 2021

Revised: 01 February 2021

Received: 01 May 2020

Published in TKDD Volume 16, Issue 3

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Cited By

Liu AHuang YWu YLiu SLiang S(2024)Active Learning for Multi-Class Drug-Drug Interactions Prediction2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822197(1611-1615)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822197
Mo XWan BTang RDing JLiu G(2023)Attention‐based network embedding with higher‐order weights and node attributesCAAI Transactions on Intelligence Technology10.1049/cit2.12215Online publication date: 5-Apr-2023
https://doi.org/10.1049/cit2.12215
Chen LLiang S(2022)Cross-Temporal Snapshot Alignment for Dynamic Multi-Relational NetworksJournal of Physics: Conference Series10.1088/1742-6596/2253/1/0120382253:1(012038)Online publication date: 1-Apr-2022
https://doi.org/10.1088/1742-6596/2253/1/012038
Zou JDu ZZhao S(2022)Multi-granular attributed network representation learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-022-01507-913:7(2071-2087)Online publication date: 2-Mar-2022
https://doi.org/10.1007/s13042-022-01507-9

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