International Business Machines Corporation

Massachusetts Institute of Technology

Tao B Schardl

Jie Chen

Toyotaro Suzumura

Charles E Leiserson

Tengfei Ma

Giacomo Domeniconi

Aldo Pareja

Timothy Kaler

A system includes a plurality of graph convolutional networks corresponding to a plurality of time steps, each network modelling a graph including nodes and edges, and in turn including a plurality of graph convolution units; an evolving mechanism; and an output layer. Each of the units, for a given one of the time steps, takes as input a graph adjacency matrix, a node feature matrix, and a parameter matrix for a current layer, and outputs a new node feature matrix for a next highest layer. The mechanism takes as input a parameter matrix for a prior time step updates the input parameter matrix, and outputs the parameter matrix for the given time step. The output layer obtains, as input, output of each of the units for a final time step, and based on the output of each of the units for the final time step, outputs a graph solution.

Evolving graph convolutional networks for dynamic graphs

Growing community of inventors

Cambridge, MA, United States of America

Tao B Schardl