Manifold APproximation and Projection by maximizINg Graph information

Bahareh Fatemi, Soheila Molaei, Hadi Zare, Shirui Pan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Graph representation learning is an effective method to represent graph data in a low dimensional space, which facilitates graph analytic tasks. The existing graph representation learning algorithms suffer from certain constraints. Random walk based methods and graph convolutional neural networks, tend to capture graph local information and fail to preserve global structural properties of graphs. We present MAPPING (Manifold APproximation and Projection by maximizINg Graph information), an unsupervised deep efficient method for learning node representations, which is capable of synchronously capturing both local and global structural information of graphs. In line with applying graph convolutional networks to construct initial representation, the proposed approach employs an information maximization process to attain representations to capture global graph structures. Furthermore, in order to preserve graph local information, we extend a novel manifold learning technique to the field of graph learning. The output of MAPPING can be easily exploited by downstream machine learning models on graphs. We demonstrate our competitive performance on three citation benchmarks. Our approach outperforms the baseline methods significantly.

Original languageEnglish
Title of host publication25th Pacific-Asia Conference, PAKDD 2021 Virtual Event, May 11–14, 2021 Proceedings, Part III
EditorsKamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty
Place of PublicationCham Switzerland
PublisherSpringer
Pages128-140
Number of pages13
ISBN (Electronic)9783030757687
ISBN (Print)9783030757670
DOIs
Publication statusPublished - 2021
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2021 - Virtual, Delhi, India
Duration: 11 May 202114 May 2021
Conference number: 25th
https://www.pakdd2021.org (Website)
https://link.springer.com/book/10.1007/978-3-030-75765-6 (Proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12714
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2021
Abbreviated titlePAKDD 2021
Country/TerritoryIndia
CityDelhi
Period11/05/2114/05/21
Internet address

Keywords

  • Feature extraction
  • Graph embedding
  • Manifold learning
  • Representation learning

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