121 citations · 260 across the 28 of their papers we have counts for
37 papers
Local Detour Centrality: A Novel Local Centrality Measure for Weighted Networks
Haim Cohen, Yinon Nachshon, Paz M. Naim +3
Centrality, in some sense, captures the extent to which a vertex controls the flow of information in a network. Here, we propose Local Detour Centrality as a novel centrality-based…
Rewiring Networks for Graph Neural Network Training Using Discrete Geometry
Jakub Bober, Anthea Monod, Emil Saucan +1
Information over-squashing is a phenomenon of inefficient information propagation between distant nodes on networks. It is an important problem that is known to significantly impac…
Curved Markov Chain Monte Carlo for Network Learning
John Sigbeku, Emil Saucan, Anthea Monod
We present a geometrically enhanced Markov chain Monte Carlo sampler for networks based on a discrete curvature measure defined on graphs. Specifically, we incorporate the concept…
Hypernetworks: From Posets to Geometry
Emil Saucan
We show that hypernetworks can be regarded as posets which, in their turn, have a natural interpretation as simplicial complexes and, as such, are endowed with an intrinsic notion…
Geometric Sampling of Networks
Vladislav Barkanass, Jürgen Jost, Emil Saucan
Motivated by the methods and results of manifold sampling based on Ricci curvature, we propose a similar approach for networks. To this end we make appeal to three types of discret…
Network geometry and market instability
Areejit Samal, Hirdesh K. Pharasi, Sarath Jyotsna Ramaia +4
The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices…