293 citations · 461 across the 13 of their papers we have counts for
8 papers · 1 filter
Understanding Transformer Reasoning Capabilities via Graph Algorithms
Clayton Sanford, Bahare Fatemi, Ethan Hall +5
Which transformer scaling regimes are able to perfectly solve different classes of algorithmic problems? While tremendous empirical advances have been attained by transformer-based…
Let Your Graph Do the Talking: Encoding Structured Data for LLMs
Bryan Perozzi, Bahare Fatemi, Dustin Zelle +4
How can we best encode structured data into sequential form for use in large language models (LLMs)? In this work, we introduce a parameter-efficient method to explicitly represent…
Talk like a Graph: Encoding Graphs for Large Language Models
Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi
Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance.…
UGSL: A Unified Framework for Benchmarking Graph Structure Learning
Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin +5
Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of applications. While the majority of GNN applications assume that a graph structure is given, so…
HUGE: Huge Unsupervised Graph Embeddings with TPUs
Brandon Mayer, Anton Tsitsulin, Hendrik Fichtenberger +2
Graphs are a representation of structured data that captures the relationships between sets of objects. With the ubiquity of available network data, there is increasing industrial…
Stars: Tera-Scale Graph Building for Clustering and Graph Learning
CJ Carey, Jonathan Halcrow, Rajesh Jayaram +3
A fundamental procedure in the analysis of massive datasets is the construction of similarity graphs. Such graphs play a key role for many downstream tasks, including clustering, c…
TF-GNN: Graph Neural Networks in TensorFlow
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais +24
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that…
Grale: Designing Networks for Graph Learning
Jonathan Halcrow, Alexandru Moşoi, Sam Ruth +1
How can we find the right graph for semi-supervised learning? In real world applications, the choice of which edges to use for computation is the first step in any graph learning p…