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Graph Embedding For Link Prediction Using Residual Variational Graph Autoencoders | IEEE Conference Publication | IEEE Xplore

Graph Embedding For Link Prediction Using Residual Variational Graph Autoencoders


Abstract:

Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protectin...Show More

Abstract:

Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph's properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph autoencoder's performance utilizing residual connections. Our method's performance is evaluated on the link prediction task. The results demonstrate that our model can achieve better results than graph convolutional neural network (GCN) and variational graph autoencoder (VGAE).
Date of Conference: 05-07 October 2020
Date Added to IEEE Xplore: 07 January 2021
ISBN Information:
Print on Demand(PoD) ISSN: 2165-0608
Conference Location: Gaziantep, Turkey

Funding Agency:


References

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