21 citations · 34 across the 6 of their papers we have counts for
6 papers
Long Range Propagation on Continuous-Time Dynamic Graphs
Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio +2
Learning Continuous-Time Dynamic Graphs (C-TDGs) requires accurately modeling spatio-temporal information on streams of irregularly sampled events. While many methods have been pro…
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…
OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
Jiawen Xu, Claas Grohnfeldt, Odej Kao
In most works on deep incremental learning research, it is assumed that novel samples are pre-identified for neural network retraining. However, practical deep classifiers often mi…
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…
Active TLS Stack Fingerprinting: Characterizing TLS Server Deployments at Scale
Markus Sosnowski, Johannes Zirngibl, Patrick Sattler +4
Active measurements can be used to collect server characteristics on a large scale. This kind of metadata can help discovering hidden relations and commonalities among server deplo…