3 papers
cs.LG2026
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…
cs.LG2024
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…
cs.LG2024
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…