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An adversarial learning approach for discovering social relations in human-centered information networks

  • Cui, Shicheng1
  • Li, Qianmu1, 2, 3
  • Chen, Shu-Ching4
  • 1 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China , Nanjing (China)
  • 2 School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China , Nanjing (China)
  • 3 Intelligent Manufacturing Department, Wuyi University, Jiangmen,, 529020, China , Jiangmen, (China)
  • 4 School of Computing and Information Sciences, Florida International University, Miami, 33199, USA , Miami (United States)
Published Article
EURASIP Journal on Wireless Communications and Networking
Springer International Publishing
Publication Date
Sep 07, 2020
DOI: 10.1186/s13638-020-01782-6
Springer Nature


The analytics on graph-structured data in cyber spaces has advanced many human-centered computing technologies. However, if only utilizing the structural properties, we might be prohibited from unraveling unknown social relations of nodes especially in the structureless networked systems. Up-to-date ways to unfold latent relationships from graph-structured data are network representation learning (NRL) techniques, but it is difficult for most existing ones to deal with the network-structureless situations due to the fact that they largely depend on the observed connections. With the ever-broader spectrum of human-centered networked systems, large quantities of textual information have been generated and collected from social and physical spaces, which may provide the clues of hidden social relations. In order to discover latent social relations from the accompanied text resources, this paper attempts to bridge the gap between text data and graph-structured data so that the textual information can be encoded to substitute for those incomplete structural information. Generative adversarial networks (GANs) are employed in the cross-modal framework to make the transformed data indistinguishable in graph-domain space and also capable of depicting structure-aware relationships with network homophily. Experiments conducted on three text-based network benchmarks demonstrate that our approach can reveal more realistic social relations from text-domain information compared against the state-of-the-art baselines.

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