6 citations · 24 across the 21 of their papers we have counts for
11 papers · 1 filter
TopoEmbedX: A General Framework for Representation Learning on Topological Domains
Florian Frantzen, Ibrahem AlJabea, Ines Henriques-Cadby +3
Topological structures such as simplicial complexes, hypergraphs, and cell complexes extend standard graph models by modeling higher-order relationships. These structures appear in…
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…
Global Ground Metric Learning with Applications to scRNA data
Damin Kühn, Michael T. Schaub
Optimal transport provides a robust framework for comparing probability distributions. Its effectiveness is significantly influenced by the choice of the underlying ground metric.…
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection
Florian Frantzen, Michael T. Schaub
In this paper, we propose HLSAD, a novel method for detecting anomalies in time-evolving simplicial complexes. While traditional graph anomaly detection techniques have been extens…
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…
Learning From Simplicial Data Based on Random Walks and 1D Convolutions
Florian Frantzen, Michael T. Schaub
Triggered by limitations of graph-based deep learning methods in terms of computational expressivity and model flexibility, recent years have seen a surge of interest in computatio…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen +40
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: h…
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou +3
Graph-based signal processing techniques have become essential for handling data in non-Euclidean spaces. However, there is a growing awareness that these graph models might need t…
ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon, Mustafa Hajij, Helen Jenne +53
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competi…
Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks
James S. Nagai, Ivan G. Costa, Michael T. Schaub
Comparing graphs by means of optimal transport has recently gained significant attention, as the distances induced by optimal transport provide both a principled metric between gra…