12 papers
A repository for discovery and reuse of higher-order network datasets
Florian Frantzen, Michael T. Schaub
Higher-order network datasets are dispersed across publications, institutional archives, and software-specific collections, making them difficult to discover, compare, and reuse. W…
Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
Josef Hoppe, Sarra Bouchkati, Farah Nasr +7
Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requires curative curtai…
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
Riccardo Porcedda, Giuseppe Squillace, Bastian Epping +4
While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood information, the performance of…
Random Abstract Cell Complexes
Josef Hoppe, Michael T. Schaub
We define a model for random (abstract) cell complexes (CCs), similiar to the well-known ErdÅs-Rényi model for graphs and its extensions for simplicial complexes. To build a rand…
Faster Inference of Cell Complexes from Flows via Matrix Factorization
Til Spreuer, Josef Hoppe, Michael T. Schaub
We consider the following inference problem: Given a set of edge-flow signals observed on a graph, lift the graph to a cell complex, such that the observed edge-flow signals can be…
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…