Graph Embedding Framework Based on Adversarial and Random Walk Regularization

Graph Embedding Framework Based on Adversarial and Random Walk Regularization

Graph embedding aims to represent node structural as well as attribute information into a low-dimensional vector space so that some downstream application tasks such as node classification, link prediction, community detection, and recommendation can be easily performed by using simple machine learn...

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Bibliographic Details
Main Authors: Wei Dou, Weiyu Zhang, Ziqiang Weng, Zhongxiu Xia
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Graph embedding
graph convolutional network
random walk
adversarial scheme
Online Access:https://ieeexplore.ieee.org/document/9306765/

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