November 14, 2018 12:49


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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Date November 27, 2018 (Tue) 14:00 - 15:30


〒103-0027 Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi,Chuo-ku, Tokyo