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Dustin Zelle
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2020 – today
- 2024
- [i6]Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Seyed Mehran Kazemi, Rami Al-Rfou, Jonathan Halcrow:
Let Your Graph Do the Talking: Encoding Structured Data for LLMs. CoRR abs/2402.05862 (2024) - 2023
- [c3]Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi:
Learning Large Graph Property Prediction via Graph Segment Training. NeurIPS 2023 - [i5]Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi:
Learning Large Graph Property Prediction via Graph Segment Training. CoRR abs/2305.12322 (2023) - [i4]Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Seyed Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, Bryan Perozzi:
UGSL: A Unified Framework for Benchmarking Graph Structure Learning. CoRR abs/2308.10737 (2023) - 2022
- [c2]Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi:
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks. NeurIPS 2022 - [i3]Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi:
Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks. CoRR abs/2203.02018 (2022) - [i2]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, Silvio Lattanzi, André Linhares, Brandon A. Mayer, Vahab S. Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi:
TF-GNN: Graph Neural Networks in TensorFlow. CoRR abs/2207.03522 (2022)
2010 – 2019
- 2019
- [c1]Rami Al-Rfou, Bryan Perozzi, Dustin Zelle:
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. WWW 2019: 37-48 - [i1]Rami Al-Rfou, Dustin Zelle, Bryan Perozzi:
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. CoRR abs/1904.09671 (2019)
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