MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. - Sami Abu-El-Haija; Bryan Perozzi; Amol Kapoor; Nazanin Alipourfard; Kristina Lerman; Hrayr Harutyunyan; Greg Ver Steeg; Aram Galstyan (ICML, 2019)

Nostalgin: Extracting 3D City Models from Historical Image Data. - Amol Kapoor; Hunter Larco; Raimondas Kiveris (KDD, 2019)

N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. - Sami Abu-El-Haija; Amol Kapoor; Bryan Perozzi; Joonseok Lee (UAI, 2019)

Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks. - Ben Adlam; Charles Weill; Amol Kapoor (CoRR, 2019)

Scaling Graph Neural Networks with Approximate PageRank. - Aleksandar Bojchevski; Johannes Klicpera; Bryan Perozzi; Amol Kapoor; Martin Blais; Benedek Rózemberczki; Michal Lukasik; Stephan Günnemann (KDD, 2020)

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks. - Amol Kapoor; Xue Ben; Luyang Liu; Bryan Perozzi; Matt Barnes 001; Martin Blais; Shawn O'Banion (CoRR, 2020)

Pathfinder Discovery Networks for Neural Message Passing. - Benedek Rozemberczki; Peter Englert; Amol Kapoor; Martin Blais; Bryan Perozzi (WWW, 2021)

Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces. - Sarah Di; Robin Yu; Amol Kapoor (CoRR, 2021)