DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. - Rami Al-Rfou; Bryan Perozzi; Dustin Zelle (WWW, 2019)

Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks. - Minji Yoon; John Palowitch; Dustin Zelle; Ziniu Hu; Ruslan Salakhutdinov; Bryan Perozzi (NeurIPS, 2022)

Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks. - Minji Yoon; John Palowitch; Dustin Zelle; Ziniu Hu; Ruslan Salakhutdinov; Bryan Perozzi (CoRR, 2022)

TF-GNN: Graph Neural Networks in TensorFlow. - Oleksandr Ferludin; Arno Eigenwillig; Martin Blais; Dustin Zelle; Jan Pfeifer; Alvaro Sanchez-Gonzalez; Wai Lok Sibon Li; Sami Abu-El-Haija; Peter W. Battaglia; Neslihan Bulut; Jonathan Halcrow; Filipe Miguel Gonçalves de Almeida; et al. (CoRR, 2022)

Learning Large Graph Property Prediction via Graph Segment Training. - Kaidi Cao; Phitchaya Mangpo Phothilimthana; Sami Abu-El-Haija; Dustin Zelle; Yanqi Zhou; Charith Mendis; Jure Leskovec; Bryan Perozzi (NeurIPS, 2023)

UGSL: A Unified Framework for Benchmarking Graph Structure Learning. - Bahare Fatemi; Sami Abu-El-Haija; Anton Tsitsulin; Seyed Mehran Kazemi; Dustin Zelle; Neslihan Bulut; Jonathan Halcrow; Bryan Perozzi (CoRR, 2023)

Let Your Graph Do the Talking: Encoding Structured Data for LLMs. - Bryan Perozzi; Bahare Fatemi; Dustin Zelle; Anton Tsitsulin; Seyed Mehran Kazemi; Rami Al-Rfou; Jonathan Halcrow (CoRR, 2024)