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Award ID contains: 2046086

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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. ; ; ; ; ; ; ; (, Resource Efficient Learning Workshop at The Web Conference (TheWebConf) (2025))
    Free, publicly-accessible full text available April 29, 2026
  3. ; ; ; ; ; (, The Second Conference on Parsimony and Learning (Proceedings Track))
    Free, publicly-accessible full text available March 24, 2026
  4. ; ; (, AAAI 2025 Bridge on Knowledge-Guided Machine Learning)
    Free, publicly-accessible full text available February 25, 2026
  5. ; ; ; ; ; ; (, Springer Nature Singapore)
    Free, publicly-accessible full text available January 1, 2026
  6. ; (, Society for Industrial and Applied Mathematics)
    Free, publicly-accessible full text available January 1, 2026
  7. ; (, IEEE)
    Free, publicly-accessible full text available December 15, 2025
  8. ; ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available December 15, 2025
  9. ; (, Data Mining and Knowledge Discovery)
    Free, publicly-accessible full text available November 1, 2025
  10. ; ; ; ; ; ; ; (, Cell Reports)
    Free, publicly-accessible full text available November 1, 2025