59 citations · 81 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 16 cited
Graph Inductive Biases in Transformers without Message Passing
Liheng Ma, Chen Lin, Derek Lim +5
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works…
cs.LG2021★ 59 cited
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Derek Lim, Felix Hohne, Xiuyu Li +4
Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networ…
cs.LG2020★ 6 cited
Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka
While message passing Graph Neural Networks (GNNs) have become increasingly popular architectures for learning with graphs, recent works have revealed important shortcomings in the…