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WalkGAN: Network Representation Learning With Sequence-Based Generative Adversarial Networks
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AI
DOI：10.1109/TNNLS.2022.3208914.png)
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摘要
En 中文
Network representation learning, also known as network embedding, aims to learn the low-dimensional representations of vertices while capturing and preserving the network structure. For real-world networks, the edges that represent some important relationships between the vertices of a network may be missed and may result in degenerated performance. The existing methods usually treat missing edges as negative samples, thereby ignoring the true connections between two vertices in a network. To capture the true network structure effectively, we propose a novel network representation learning method called WalkGAN, where random walk scheme and generative adversarial networks (GAN) are incorporated into a network embedding framework. Specifically, WalkGAN leverages GAN to generate the synthetic sequences of the vertices that sufficiently simulate random walk on a network and further learn vertex representations from these vertex sequences. Thus, the unobserved links between the vertices are inferred with high probability instead of treating them as nonexistence. Experimental results on the benchmark network datasets demonstrate that WalkGAN achieves significant performance improvements for vertex classification, link prediction, and visualization tasks.
Keyword:
Generative adversarial networks
Representation learning
Neural networks
Generators
Feature extraction
Task analysis
Learning systems
Generative adversarial networks (GANs)
network representation
random walk
sequence
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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