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Creators/Authors contains: "Shiao, William"

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  1. ; ; ; ; ; ( , Data Mining and Knowledge Discovery)
    Abstract

    In this work, we explore multiplex graph (networks with different types of edges) generation with deep generative models. We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose TenGAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better.

     
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  2. ; ; ; ( , 2022 IEEE International Conference on Data Mining Workshops (ICDMW))
    Full Text Available 
  3. ; ( , 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA))
    Full Text Available 
  4. ; ; ; ; ; ( , Machine Learning on Graphs (MLoG) Workshop at WSDM 2022)
    Accepted Manuscript Full Text Available