Learning Graph Representations
- URL: http://arxiv.org/abs/2102.02026v1
- Date: Wed, 3 Feb 2021 12:07:55 GMT
- Title: Learning Graph Representations
- Authors: Rucha Bhalchandra Joshi and Subhankar Mishra
- Abstract summary: Graph Neural Networks (GNNs) are efficient ways to get insight into large dynamic graph datasets.
In this paper, we discuss the graph convolutional neural networks graph autoencoders and Social-temporal graph neural networks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Social and information networks are gaining huge popularity recently due to
their various applications. Knowledge representation through graphs in the form
of nodes and edges should preserve as many characteristics of the original data
as possible. Some of the interesting and useful applications on these graphs
are graph classification, node classification, link prediction, etc. The Graph
Neural Networks have evolved over the last few years. Graph Neural Networks
(GNNs) are efficient ways to get insight into large and dynamic graph datasets
capturing relationships among billions of entities also known as knowledge
graphs.
In this paper, we discuss the graph convolutional neural networks graph
autoencoders and spatio-temporal graph neural networks. The representations of
the graph in lower dimensions can be learned using these methods. The
representations in lower dimensions can be used further for downstream machine
learning tasks.
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