14 papers
The impact of the path ensemble on path percolation
Yunhao Ding, Andreas Münch, Renaud Lambiotte
Traffic-induced failures, from packet loss in communication networks to congestion breakdown in transport systems, occur when flows progressively exhaust the edges they traverse. P…
Permutation-Invariant Spectral Learning via Dyson Diffusion
Tassilo Schwarz, Cai Dieball, Constantin Kogler +4
Diffusion models are central to generative modeling and have been adapted to graphs by diffusing adjacency matrix representations. The challenge of having up to such represent…
From First Principles to Multi-scale Decomposition:Mutual Information as a Segregation Index
Rohit Sahasrabuddhe, Renaud Lambiotte
Segregation is a multi-scale phenomenon that requires careful measurement. A segregation index implicitly defines how the demographic compositions of locations are compared. We ide…
Needles in a haystack: using forensic network science to uncover insider trading
Gian Jaeger, Wang Ngai Yeung, Renaud Lambiotte
Although the automation and digitisation of anti-financial crime investigation has made significant progress in recent years, detecting insider trading remains a unique challenge,…
Embedding networks with the random walk first return time distribution
Vedanta Thapar, Renaud Lambiotte, George T. Cantwell
We propose the first return time distribution (FRTD) of a random walk as an interpretable and mathematically grounded node embedding. The FRTD assigns a probability mass function t…
Complex-Weighted Convolutional Networks: Provable Expressiveness via Complex Diffusion
Cristina López Amado, Tassilo Schwarz, Yu Tian +1
Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications, yet they remain limited by oversmoothing and poor performance on heterophilic graphs. To…