Conference Paper2021Cited by 1

Node embedding using mutual information and self-supervision based bi-level aggregation

Kashob Kumar Roy, Amit Roy, AKM Mahbubur Rahman, M Ashraful Amin, Amin Ahsan Ali

2021 International Joint Conference on Neural Networks (IJCNN)

IEEE, pp. 1–8

DOI: 10.1109/ijcnn52387.2021.9533715Open AccessURL

Abstract

Graph Neural Networks (GNNs) learn low dimensional representations of nodes by aggregating information from their neighborhood in graphs. However, traditional GNNs suffer from two fundamental shortcomings due to their local (l-hop neighborhood) aggregation scheme. First, not all nodes in the neighborhood carry relevant information for the target node. Since GNNs do not exclude noisy nodes in their neighborhood, irrelevant information gets aggregated, which reduces the quality of the representation. Second, traditional GNNs also fail to capture long-range non-local dependencies between nodes. To address these limitations, we exploit mutual information (MI) to define two types of neighborhood, 1) Local Neighborhood where nodes are densely connected within a community and each node would share higher MI with its neighbors, and 2) Non-Local Neighborhood where MI-based node clustering is introduced to assemble informative but graphically distant nodes in the same cluster. To generate node presentations, we combine the embeddings generated by bi-level aggregation - local aggregation to aggregate features from local neighborhoods to avoid noisy information and non-local aggregation to aggregate features from non-local neighborhoods. Furthermore, we leverage self-supervision learning to estimate MI with few labeled data. Finally, we show that our model significantly outperforms the state-of-the-art methods in a wide range of assortative and disassortative graphs11Source Code at: https://github.com/forkkr/LnL-GNN.

Keywords

  • graph neural networks
  • Mutual information
  • node embeddings
  • self-supervised learning
  • long-range dependencies
  • node clustering

CCDS Authors

Kashob Kumar Roy
Kashob Kumar Roy

Kashob Kumar Roy

  • Research Assistant, CCDS

PhD Student

University of Illinois Urbana-Champaign

Published at

PAKDD, IJCNN

Research Interests

Machine Learning

Amit Roy
Amit Roy

Amit Roy

  • Research Assistant, CCDS

PhD Student

Purdue University

Published at

PAKDD, IJCNN

Research Interests

Machine Learning

AKM Mahbubur Rahman, PhD
AKM Mahbubur Rhman, PhD

AKM Mahbubur Rahman, PhD

  • Director, DS & IPT Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, ECCV, WACV, PAKDD, LREC, IJCNN, ICPR, ICIP, HCII, BEA, Scientific Reports, Information Sciences

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

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Cited By

  1. 1.Sun, Feng, Kumar, Ajith, Yang, Guanci, Zhu, Qikui, Zhang, Yiyun, Zhang, Ansi, Makwana, Dhruv. (2021). RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks. arXiv (Cornell University)[10.48550/arxiv.2108.07481]
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