Neural Representation Learning for Graphs
Graph-structured data is ubiquitous and occurs in numerous application domains. The talk will provide an overview of graph representation learning approaches such a graph convolutional networks. We show that these approaches can be understood from two different perspectives: as a special case of tensor factorizations and as instances of a class of algorithms that learn from local graph structures such as paths and neighborhoods. The talk will also discuss current work of our group including applications of graph neural networks.
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