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.

More Information

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(Google Maps)