Discovering Structural Hole Spanners in Dynamic Networks via Graph Neural Networks
Author:
Affiliation:
1. University of Adelaide,School of Computer Science,Australia
2. Sun Yat-Sen University,School of Computer Science and Engineering,Guangzhou,China
3. Griffith University,School of Information and Communication Technology,Australia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10101871/10101874/10102045.pdf?arnumber=10102045
Reference31 articles.
1. Attention-based graph neural network for semi-supervised learning;thekumparampil,2018
2. Maintenance of Structural Hole Spanners in Dynamic Networks
3. A social community detection algorithm based on parallel grey label propagation
4. Semi-supervised classification with graph convolutional networks;kipf;International Conference on Learning Representations (ICLR),2017
5. Toward Unsupervised Graph Neural Network: Interactive Clustering and Embedding via Optimal Transport
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1. Effective graph-neural-network based models for discovering Structural Hole Spanners in large-scale and diverse networks;Expert Systems with Applications;2024-09
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