16 citations · 52 across the 9 of their papers we have counts for
12 papers
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…
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…
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…
Topological Trajectory Classification and Landmark Inference on Simplicial Complexes
Vincent P. Grande, Josef Hoppe, Florian Frantzen +1
We consider the problem of classifying trajectories on a discrete or discretised 2-dimensional manifold modelled by a simplicial complex. Previous works have proposed to project th…
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…
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…
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…
Signal Processing on Product Spaces
T. Mitchell Roddenberry, Vincent P. Grande, Florian Frantzen +2
We establish a framework for signal processing on product spaces of simplicial and cellular complexes. For simplicity, we focus on the product of two complexes representing time an…
Outlier Detection for Trajectories via Flow-embeddings
Florian Frantzen, Jean-Baptiste Seby, Michael T. Schaub
We propose a method to detect outliers in empirically observed trajectories on a discrete or discretized manifold modeled by a simplicial complex. Our approach is similar to spectr…
Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes
T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub +1
We develop wavelet representations for edge-flows on simplicial complexes, using ideas rooted in combinatorial Hodge theory and spectral graph wavelets. We first show that the Hodg…
Signal processing on simplicial complexes
Michael T. Schaub, Jean-Baptiste Seby, Florian Frantzen +3
Higher-order networks have so far been considered primarily in the context of studying the structure of complex systems, i.e., the higher-order or multi-way relations connecting th…
The Effects of Randomness on the Stability of Node Embeddings
Tobias Schumacher, Hinrikus Wolf, Martin Ritzert +6
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i.e., the random variation of their outcomes given identical algorith…