667 citations · 844 across the 28 of their papers we have counts for
8 papers · 1 filter
BScNets: Block Simplicial Complex Neural Networks
Yuzhou Chen, Yulia R. Gel, H. Vincent Poor
Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning which expands the idea of convolutional architectures from node space to simplicial…
Topological Relational Learning on Graphs
Yuzhou Chen, Baris Coskunuzer, Yulia R. Gel
Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are…
Using NASA Satellite Data Sources and Geometric Deep Learning to Uncover Hidden Patterns in COVID-19 Clinical Severity
Ignacio Segovia-Dominguez, Huikyo Lee, Zhiwei Zhen +5
As multiple adverse events in 2021 illustrated, virtually all aspects of our societal functioning -- from water and food security to energy supply to healthcare -- more than ever d…
Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks
Dorcas Ofori-Boateng, Ignacio Segovia Dominguez, Murat Kantarcioglu +2
Motivated by the recent surge of criminal activities with cross-cryptocurrency trades, we introduce a new topological perspective to structural anomaly detection in dynamic multila…
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
Yuzhou Chen, Ignacio Segovia-Dominguez, Yulia R. Gel
There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block…
Smart Vectorizations for Single and Multiparameter Persistence
Baris Coskunuzer, CUneyt Gurcan Akcora, Ignacio Segovia Dominguez +3
The machinery of topological data analysis becomes increasingly popular in a broad range of machine learning tasks, ranging from anomaly detection and manifold learning to graph cl…