Journal
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING
Volume -, Issue -, Pages 120-129Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3292500.3330970
Keywords
Heterogeneous Information Network; Network Embedding; Generative Adversarial Network
Funding
- National Natural Science Foundation of China [61772082, 61702296]
- National Key Research and Development Program of China [2017YFB0803304]
- Beijing Municipal Natural Science Foundation [4182043]
- Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant [18-C220-SMU-006]
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Network embedding, which aims to represent network data in a low-dimensional space, has been commonly adopted for analyzing heterogeneous information networks (HIN). Although exiting HIN embedding methods have achieved performance improvement to some extent, they still face a few major weaknesses. Most importantly, they usually adopt negative sampling to randomly select nodes from the network, and they do not learn the underlying distribution for more robust embedding. Inspired by generative adversarial networks (GAN), we develop a novel framework HeGAN for HIN embedding, which trains both a discriminator and a generator in a minimax game. Compared to existing HIN embedding methods, our generator would learn the node distribution to generate better negative samples. Compared to GANs on homogeneous networks, our discriminator and generator are designed to be relation-aware in order to capture the rich semantics on HINs. Furthermore, towards more effective and efficient sampling, we propose a generalized generator, which samples latent nodes directly from a continuous distribution, not confined to the nodes in the original network as existing methods are. Finally, we conduct extensive experiments on four real-world datasets. Results show that we consistently and significantly outperform state-of-the-art baselines across all datasets and tasks.
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