555 citations · 2.9k across the 55 of their papers we have counts for
7 papers · 1 filter
Higher-order signal processing with the Dirac operator
Lucille Calmon, Michael T. Schaub, Ginestra Bianconi
The processing of signals on simplicial and cellular complexes defined by nodes, edges, and higher-order cells has recently emerged as a principled extension of graph signal proces…
Neighborhood Structure Configuration Models
Felix I. Stamm, Michael Scholkemper, Markus Strohmaier +1
We develop a new method to efficiently sample synthetic networks that preserve the d-hop neighborhood structure of a given network for any given d. The proposed algorithm trades of…
Improving the visibility of minorities through network growth interventions
Leonie Neuhäuser, Fariba Karimi, Jan Bachmann +2
Improving the position of minorities in networks via interventions is a challenge of high theoretical and societal importance. In this work, we examine how different network growth…
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
Donald Loveland, Jiong Zhu, Mark Heimann +3
We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity…
Topological Deep Learning: Going Beyond Graph Data
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou +11
Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes…
Blind Extraction of Equitable Partitions from Graph Signals
Michael Scholkemper, Michael Schaub
Finding equitable partitions is closely related to the extraction of graph symmetries and of interest in a variety of applications context such as node role detection, cluster sync…