21 citations · 26 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 21 cited
GCNH: A Simple Method For Representation Learning On Heterophilous Graphs
Andrea Cavallo, Claas Grohnfeldt, Michele Russo +2
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in which edges tend to connect nodes of the same type. Yet, achievement of consistent…
cs.LG2022★ 5 cited
2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance
Andrea Cavallo, Claas Grohnfeldt, Michele Russo +2
Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data across numerous domains. Their underlying ability to represent nodes as summaries of thei…