4 papers
SeedER: Seed-and-Expand Retrieval from Knowledge Graphs
Hamed Shirzad, Frederik Wenkel, Dominique Beaini +2
Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph expansion grows rapidly, and dense…
TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction
Frederik Wenkel, Wilson Tu, Cassandra Masschelein +12
Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet exhaustively exploring th…
Even Sparser Graph Transformers
Hamed Shirzad, Honghao Lin, Balaji Venkatachalam +3
Graph Transformers excel in long-range dependency modeling, but generally require quadratic memory complexity in the number of nodes in an input graph, and hence have trouble scali…
A Theory for Compressibility of Graph Transformers for Transductive Learning
Hamed Shirzad, Honghao Lin, Ameya Velingker +3
Transductive tasks on graphs differ fundamentally from typical supervised machine learning tasks, as the independent and identically distributed (i.i.d.) assumption does not hold a…