no code implementations • 25 Sep 2019 • John Palowitch, Bryan Perozzi
In this paper, we show that when metadata is correlated with the formation of node neighborhoods, unsupervised node embedding dimensions learn this metadata.
no code implementations • NeurIPS 2023 • Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.
1 code implementation • 10 Sep 2020 • Miheer Dewaskar, John Palowitch, Mark He, Michael I. Love, Andrew B. Nobel
A common problem in the exploratory analysis of such data is to identify groups of features of different data types that are strongly associated.
1 code implementation • 8 Aug 2021 • Di Huang, Jacob Bartel, John Palowitch
The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content.
1 code implementation • 28 Feb 2022 • John Palowitch, Anton Tsitsulin, Brandon Mayer, Bryan Perozzi
Using GraphWorld, a user has fine-grained control over graph generator parameters, and can benchmark arbitrary GNN models with built-in hyperparameter tuning.
1 code implementation • 3 Mar 2022 • Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi
We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG.
1 code implementation • 4 Apr 2022 • Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, Bryan Perozzi
This shockingly small sample size (~10) allows for only limited scientific insight into the problem.
1 code implementation • 7 Jul 2022 • Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Wai Lok Sibon Li, Sami Abu-El-Haija, Peter Battaglia, Neslihan Bulut, Jonathan Halcrow, Filipe Miguel Gonçalves de Almeida, Pedro Gonnet, Liangze Jiang, Parth Kothari, Silvio Lattanzi, André Linhares, Brandon Mayer, Vahab Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
1 code implementation • 10 Jul 2022 • Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov
As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems.
1 code implementation • 17 Jul 2023 • Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh, Bryan Perozzi
In this work we examine how two additional synthetic graph generators can improve GraphWorld's evaluation; LFR, a well-established model in the graph clustering literature and CABAM, a recent adaptation of the Barabasi-Albert model tailored for GNN benchmarking.
1 code implementation • 26 Feb 2024 • Tzuf Paz-Argaman, Sayali Kulkarni, John Palowitch, Jason Baldridge, Reut Tsarfaty
Current navigation studies concentrate on egocentric local descriptions (e. g., `it will be on your right') that require reasoning over the agent's local perception.