4 papers
HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations
Guillermo Bernárdez, Marco Montagna, Louis Van Langendonck +7
While Graph Neural Networks (GNNs) have proven highly effective at modeling relational data, pairwise connections cannot fully capture multi-way relationships naturally present in…
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
Louisa Cornelis, Johan Mathe, Louis Van Langendonck +2
Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime , where the number of graphs $n…
TopoBench: A Framework for Benchmarking Topological Deep Learning
Lev Telyatnikov, Guillermo Bernardez, Marco Montagna +34
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL int…
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design
Lev Telyatnikov, Maria Sofia Bucarelli, Guillermo Bernardez +3
Most of the current hypergraph learning methodologies and benchmarking datasets in the hypergraph realm are obtained by lifting procedures from their graph analogs, leading to over…