9 citations · 32 across the 14 of their papers we have counts for
15 papers
Ollivier's Ricci Curvature on Complex-weighted Graphs
Yu Tian, Eleanor Wiesler, Melanie Weber
Understanding the geometry of complex networks is critical for effective modeling and analysis across domains. While discrete notions of Ricci curvature have emerged as powerful to…
Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature
Lukas Fesser, Melanie Weber
While Graph Neural Networks (GNNs) have been successfully leveraged for learning on graph-structured data across domains, several potential pitfalls have been described recently. T…
Curvature-based Clustering on Graphs
Yu Tian, Zachary Lubberts, Melanie Weber
Unsupervised node clustering (or community detection) is a classical graph learning task. In this paper, we study algorithms, which exploit the geometry of the graph to identify de…
Augmentations of Forman's Ricci Curvature and their Applications in Community Detection
Lukas Fesser, Sergio Serrano de Haro Iváñez, Karel Devriendt +2
The notion of curvature on graphs has recently gained traction in the networks community, with the Ollivier-Ricci curvature (ORC) in particular being used for several tasks in netw…
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension
Wenyue Hua, Yuchen Zhang, Zhe Chen +2
The application of Natural Language Processing (NLP) to specialized domains, such as the law, has recently received a surge of interest. As many legal services rely on processing a…
Identifying biases in legal data: An algorithmic fairness perspective
Jackson Sargent, Melanie Weber
The need to address representation biases and sentencing disparities in legal case data has long been recognized. Here, we study the problem of identifying and measuring biases in…
Neighborhood Growth Determines Geometric Priors for Relational Representation Learning
Melanie Weber
The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of representation learning. While there exists a large body of literature on…
Projection-free nonconvex stochastic optimization on Riemannian manifolds
Melanie Weber, Suvrit Sra
We study stochastic projection-free methods for constrained optimization of smooth functions on Riemannian manifolds, i.e., with additional constraints beyond the parameter domain…
The Oracle of DLphi
Dominik Alfke, Weston Baines, Jan Blechschmidt +24
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of l…
Forman's Ricci curvature - From networks to hypernetworks
Emil Saucan, Melanie Weber
Networks and their higher order generalizations, such as hypernetworks or multiplex networks are ever more popular models in the applied sciences. However, methods developed for th…
Heuristic Framework for Multi-Scale Testing of the Multi-Manifold Hypothesis
F. Patricia Medina, Linda Ness, Melanie Weber +1
When analyzing empirical data, we often find that global linear models overestimate the number of parameters required. In such cases, we may ask whether the data lies on or near a…
Associative Memory Impairments arising from Neurodegenerative Diseases and Traumatic Brain Injuries in a Hopfield Network Model
Melanie Weber, Pedro D. Maia, J. Nathan Kutz
Neurodegenerative diseases and traumatic brain injuries (TBI) are among the main causes of cognitive dysfunction in humans. Both manifestations exhibit the extensive presence of fo…
Can one see the shape of a network?
Melanie Weber, Emil Saucan, Jürgen Jost
Traditionally, network analysis is based on local properties of vertices, like their degree or clustering, and their statistical behavior across the network in question. This paper…
Characterizing Complex Networks with Forman-Ricci Curvature and Associated Geometric Flows
Melanie Weber, Emil Saucan, Jürgen Jost
We introduce Forman-Ricci curvature and its corresponding flow as characteristics for complex networks attempting to extend the common approach of node-based network analysis by ed…
Forman-Ricci flow for change detection in large dynamic data sets
Melanie Weber, Jürgen Jost, Emil Saucan
We present a viable solution to the challenging question of change detection in complex networks inferred from large dynamic data sets. Building on Forman's discretization of the c…