145 citations · 365 across the 24 of their papers we have counts for
5 papers · 1 filter
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…
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…
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models
Mustafa Yasir, John Palowitch, Anton Tsitsulin +2
Despite a surge in interest in GNN development, homogeneity in benchmarking datasets still presents a fundamental issue to GNN research. GraphWorld is a recent solution which uses…
Unsupervised Embedding Quality Evaluation
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches. Despite numerous successes and approaching supervised-level p…