Proceedings

TAXOGAN: Hierarchical Network Representation Learning via Taxonomy Guided Generative Adversarial Networks (Extended Abstract)

Carl Yang, Jieyu Zhang, Jiawei Han

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers. Pages 4859-4863.

https://doi.org/10.24963/ijcai.2021/666

Keywords

  • Data Mining: Mining Graphs, Semi Structured Data, Complex Data
  • Knowledge Representation and Reasoning: Leveraging Knowledge and Learning
  • Machine Learning: Knowledge Aided Learning
  • Machine Learning Applications: Networks

Abstract

Network representation learning aims at transferring node proximity in networks into distributed vectors, which can be leveraged in various downstream applications. Recent research has shown that nodes in a network can often be organized in latent hierarchical structures, but without a particular underlying taxonomy, the learned node embedding is less useful nor interpretable. In this work, we aim to improve network embedding by modeling the conditional node proximity in networks indicated by node labels residing in real taxonomies. In the meantime, we also aim to model the hierarchical label proximity in the given taxonomies, which is too coarse by solely looking at the hierarchical topologies. Comprehensive experiments and case studies demonstrate the utility of TAXOGAN.

Carl Yang, Jieyu Zhang, and Jiawei Han. 2021. TAXOGAN: Hierarchical Network Representation Learning via Taxonomy Guided Generative Adversarial Networks (Extended Abstract). In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4859–4863. https://doi.org/10.24963/ijcai.2021/666
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