A Robust and Generalized Framework for Adversarial Graph Embedding
- URL: http://arxiv.org/abs/2105.10651v1
- Date: Sat, 22 May 2021 07:05:48 GMT
- Title: A Robust and Generalized Framework for Adversarial Graph Embedding
- Authors: Jianxin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun
Sun, Philip S. Yu, Lifang He
- Abstract summary: We propose a robust framework for adversarial graph embedding, named AGE.
AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution.
Based on this framework, we propose three models to handle three types of graph data.
- Score: 73.37228022428663
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph embedding is essential for graph mining tasks. With the prevalence of
graph data in real-world applications, many methods have been proposed in
recent years to learn high-quality graph embedding vectors various types of
graphs. However, most existing methods usually randomly select the negative
samples from the original graph to enhance the training data without
considering the noise. In addition, most of these methods only focus on the
explicit graph structures and cannot fully capture complex semantics of edges
such as various relationships or asymmetry. In order to address these issues,
we propose a robust and generalized framework for adversarial graph embedding
based on generative adversarial networks. Inspired by generative adversarial
network, we propose a robust and generalized framework for adversarial graph
embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced
negative samples from the implicit distribution, and enables the discriminator
and generator to jointly learn each node's robust and generalized
representation. Based on this framework, we propose three models to handle
three types of graph data and derive the corresponding optimization algorithms,
i.e., UG-AGE and DG-AGE for undirected and directed homogeneous graphs,
respectively, and HIN-AGE for heterogeneous information networks. Extensive
experiments show that our methods consistently and significantly outperform
existing state-of-the-art methods across multiple graph mining tasks, including
link prediction, node classification, and graph reconstruction.
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