5 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
The Expressive Power of Random-Feature MPNNs Across Graph Sizes
Robert R. Nerem, Mitchell Black, Antonis Vasileiou +2
Continuous random node features increase the expressivity of message-passing neural networks (MPNNs), but existing theoretical analyses are not fine-grained enough to capture their…
Understanding Truncated Positional Encodings for Graph Neural Networks
James Flora, Mitchell Black, Weng-Keen Wong +1
Positional encodings (PEs) enhance the power of graph neural networks (GNNs), both theoretically and empirically. Two of the most popular families of PEs - spectral (e.g., Laplacia…
Comparing Graph Transformers via Positional Encodings
Mitchell Black, Zhengchao Wan, Gal Mishne +2
The distinguishing power of graph transformers is closely tied to the choice of positional encoding: features used to augment the base transformer with information about the graph.…
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
Mitchell Black, Zhengchao Wan, Amir Nayyeri +1
Message passing graph neural networks (GNNs) are a popular learning architectures for graph-structured data. However, one problem GNNs experience is oversquashing, where a GNN has…