270 citations · 807 across the 14 of their papers we have counts for
5 papers · 1 filter
InstantEmbedding: Efficient Local Node Representations
Ştefan Postăvaru, Anton Tsitsulin, Filipe Miguel Gonçalves de Almeida +3
In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations. We theoretically prove that our appr…
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…
Grale: Designing Networks for Graph Learning
Jonathan Halcrow, Alexandru Moşoi, Sam Ruth +1
How can we find the right graph for semi-supervised learning? In real world applications, the choice of which edges to use for computation is the first step in any graph learning p…
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…
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Graph comparison is a fundamental operation in data mining and information retrieval. Due to the combinatorial nature of graphs, it is hard to balance the expressiveness of the sim…