Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization

Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node clustering. However, a fundamental flaw exists...

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Vydáno v:Entropy (Basel, Switzerland) Ročník 22; číslo 2; s. 197
Hlavní autoři: Choong, Jun Jin, Liu, Xin, Murata, Tsuyoshi
Médium: Journal Article
Jazyk:angličtina
Vydáno: MDPI 01.02.2020
MDPI AG
Témata:
graph neural network
network embedding
variational autoencoder
variational inference
ISSN:1099-4300, 1099-4300
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Shrnutí:Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node clustering. However, a fundamental flaw exists in Variational Autoencoder (VAE)-based approaches. Specifically, merely minimizing the loss of VAE increases the deviation from its primary objective. Focusing on Variational Graph Autoencoder for Community Detection (VGAECD) we found that optimizing the loss using the stochastic gradient descent often leads to sub-optimal community structure especially when initialized poorly. We address this shortcoming by introducing a dual optimization procedure. This procedure aims to guide the optimization process and encourage learning of the primary objective. Additionally, we linearize the encoder to reduce the number of learning parameters. The outcome is a robust algorithm that outperforms its predecessor.
Bibliografie:ObjectType-Article-1
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ISSN:1099-4300
1099-4300
DOI:10.3390/e22020197