21 citations · 34 across the 5 of their papers we have counts for
3 papers · 1 filter
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs
Tim Poštuvan, Claas Grohnfeldt, Michele Russo +1
Anomaly detection in continuous-time dynamic graphs is an emerging field yet under-explored in the context of learning algorithms. In this paper, we pioneer structured analyses of…
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…
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…