no code implementations • 7 Oct 2021 • Artun Bayer, Arindam Chowdhury, Santiago Segarra
In this context, our current work considers a challenging inductive setting where a set of labeled graphs are available for training while the unlabeled target graph is completely separate, i. e., there are no connections between labeled and unlabeled nodes.
1 code implementation • 20 Feb 2021 • Cameron R. Wolfe, Jingkang Yang, Arindam Chowdhury, Chen Dun, Artun Bayer, Santiago Segarra, Anastasios Kyrillidis
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters.