Paper Info
Title
Distribution-Induced Bidirectional Generative Adversarial Network For Graph Representation Learning
Abstract
Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many compute vision tasks. However, most existing algorithms ignore the existence of inherent data distribution and even noises. This may significantly increase the phenomenon of over-fitting and deteriorate the testing accuracy. In this paper, we propose a Distribution-induced Bidirectional Generative Adversarial Network (named DBGAN) for graph representation learning. Instead of the widely used normal distribution assumption, the prior distribution of latent representation in our DBGAN is estimated in a structure-aware way, which implicitly bridges the graph and feature spaces by prototype learning. Thus discriminative and robust representations are generated for all nodes. Furthermore, to improve their generalization ability while preserving representation ability, the sample-level and distribution-level consistency is well balanced via a bidirectional adversarial learning framework. An extensive group of experiments are then carefully designed and presented, demonstrating that our DBGAN obtains remarkably more favorable trade-off between representation and robustness, and meanwhile is dimension-efficient, over currently available alternatives in various tasks. The source code is released in https://github.com/SsGood/DBGAN.
Year
DOI
Venue
2020
10.1109/CVPR42600.2020.00725
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
26
6
Name
Order
Citations
PageRank
Shuai Zheng1124.27
Zhenfeng Zhu235736.87
Xingxing Zhang313416.05
Liu Zhizhe420.75
Jian Cheng51327115.72
Yao Zhao61926219.11