Let Your Graph Do the Talking: Encoding Structured Data for LLMs

no code implementations • 8 Feb 2024 • Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, Jonathan Halcrow

How can we best encode structured data into sequential form for use in large language models (LLMs)?

UGSL: A Unified Framework for Benchmarking Graph Structure Learning

1 code implementation • 21 Aug 2023 • Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, Bryan Perozzi

We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework.

Benchmarking Graph structure learning

Learning Large Graph Property Prediction via Graph Segment Training

1 code implementation • NeurIPS 2023 • Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi

Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint.

Graph Property Prediction Property Prediction

TF-GNN: Graph Neural Networks in TensorFlow

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.

Graph Sampling

Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks

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.

Domain Adaptation Graph Learning +2

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

1 code implementation • 21 Apr 2019 • Rami Al-Rfou, Dustin Zelle, Bryan Perozzi

Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.

Feature Engineering Graph Attention +2