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Multiple Graph Adversarial Learning
[article]
2019
arXiv
pre-print
Recently, Graph Convolutional Networks (GCNs) have been widely studied for graph-structured data representation and learning. However, in many real applications, data are coming with multiple graphs, and it is non-trivial to adapt GCNs to deal with data representation with multiple graph structures. One main challenge for multi-graph representation is how to exploit both structure information of each individual graph and correlation information across multiple graphs simultaneously. In this
arXiv:1901.07439v1
fatcat:r6ujhuegtnbotgrqnka5w2lvie