Effective Decoding in Graph Auto-Encoder Using Triadic Closure

Authors

  • Han Shi Hong Kong University of Science and Technology
  • Haozheng Fan Amazon
  • James T. Kwok Hong Kong University of Science and Technology

DOI:

https://doi.org/10.1609/aaai.v34i01.5437

Abstract

The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graph-structured data. While the encoder is often a powerful graph convolutional network, the decoder reconstructs the graph structure by only considering two nodes at a time, thus ignoring possible interactions among edges. On the other hand, structured prediction, which considers the whole graph simultaneously, is computationally expensive. In this paper, we utilize the well-known triadic closure property which is exhibited in many real-world networks. We propose the triad decoder, which considers and predicts the three edges involved in a local triad together. The triad decoder can be readily used in any graph-based auto-encoder. In particular, we incorporate this to the (variational) graph auto-encoder. Experiments on link prediction, node clustering and graph generation show that the use of triads leads to more accurate prediction, clustering and better preservation of the graph characteristics.

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Published

2020-04-03

How to Cite

Shi, H., Fan, H., & Kwok, J. T. (2020). Effective Decoding in Graph Auto-Encoder Using Triadic Closure. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 906-913. https://doi.org/10.1609/aaai.v34i01.5437

Issue

Section

AAAI Technical Track: Applications