Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2019
Abstract
Network embedding, which aims to represent network data in alow-dimensional space, has been commonly adopted for analyzingheterogeneous information networks (HIN). Although exiting HINembedding methods have achieved performance improvement tosome extent, they still face a few major weaknesses. Most importantly, they usually adopt negative sampling to randomly selectnodes 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 forHIN embedding, which trains both a discriminator and a generatorin a minimax game. Compared to existing HIN embedding methods,our generator would learn the node distribution to generate betternegative samples. Compared to GANs on homogeneous networks,our discriminator and generator are designed to be relation-aware inorder to capture the rich semantics on HINs. Furthermore, towardsmore effective and efficient sampling, we propose a generalizedgenerator, which samples “latent” nodes directly from a continuousdistribution, not confined to the nodes in the original network asexisting methods are. Finally, we conduct extensive experiments onfour real-world datasets. Results show that we consistently and significantly outperform state-of-the-art baselines across all datasetsand tasks.
Keywords
Heterogeneous Information Network, Network Embedding, Generative Adversarial Network
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
KDD '19: Proceedings of the 25th ACM SIGKDD Conference On Knowledge Discovery and Data Mining, Anchorage, Alaska, August 4-8
First Page
120
Last Page
129
ISBN
9781450362016
Identifier
10.1145/3292500.3330970
Publisher
ACM
City or Country
New York
Citation
HU, Binbin; FANG, Yuan; and SHI, Chuan.
Adversarial learning on heterogeneous information networks. (2019). KDD '19: Proceedings of the 25th ACM SIGKDD Conference On Knowledge Discovery and Data Mining, Anchorage, Alaska, August 4-8. 120-129.
Available at: https://ink.library.smu.edu.sg/sis_research/4433
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3292500.3330970
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons