375 citations · 541 across the 4 of their papers we have counts for
3 papers · 1 filter
Pathfinder Discovery Networks for Neural Message Passing
Benedek Rozemberczki, Peter Englert, Amol Kapoor +2
In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model…
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Amol Kapoor, Xue Ben, Luyang Liu +4
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecastin…
Scaling Graph Neural Networks with Approximate PageRank
Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi +5
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge - many recently propos…