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ICANE: interaction content-aware network embedding via co-embedding of nodes and edges

  • Linchuan Xu
  • Xiaokai Wei
  • Jiannong Cao
  • Philip S. Yu
Regular Paper
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Abstract

Network embedding has been increasingly employed in network analysis as it can learn node representations that encode the network structure resulting from node interactions. In this paper, we propose to embed not only the network structure, but also the interaction content within which each interaction arises. The interaction content should better be embedded in node representations because it reveals interaction preferences of the two nodes involved, and interaction preferences are essential characteristics that nodes expose in the network environment. To achieve this goal, we propose an idea of interaction content-aware network embedding via co-embedding of nodes and edges. The embedding of edges is to learn edge representations that preserve the interaction content. Then the interaction content can be incorporated into node representations through edge representations. Comprehensive empirical evaluation demonstrates that the proposed method outperforms five recent network embedding models in applications including visualization, link prediction and classification.

Keywords

Network embedding Representation learning Data mining 

Notes

Acknowledgements

The work described in this paper was partially supported by the National Key R&D Program of China - 2018 YFB1004801, RGC General Research Fund under Grant PolyU 152199/17E, the funding for Project of Strategic Importance provided by the Hong Kong Polytechnic University (Project Code: 1-ZE26), NSF through Grants IIS-1526499, IIS-1763325, and CNS-1626432, and NSFC 61672313.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityHung HomHong Kong
  2. 2.Facebook Inc.Menlo ParkUSA
  3. 3.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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