8.8k citations · 11k across the 60 of their papers we have counts for
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The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure
Anton Tsitsulin, Bryan Perozzi
Graph learning methods help utilize implicit relationships among data items, thereby reducing training label requirements and improving task performance. However, determining the o…
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…
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao +4
Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal…
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…
Explaining and Adapting Graph Conditional Shift
Qi Zhu, Yizhu Jiao, Natalia Ponomareva +2
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution sh…