Nature

Binomial Edge Ideals And Graph Theory

Binomial edge ideals constitute a vibrant research area where the methods of commutative algebra intersect with the combinatorial structures inherent in graph theory. By associating to each graph an ...

Probability Distribution Methods to Predict Stock Profits

Discover how probability distribution methods can help predict stock market returns and improve investment decisions. Learn to assess risk and potential gains.
IEEE

Gen-GraphEx: Generative In-Distribution Graph Explanations for Time-Efficient Model-Level Interpretability of GNNs

Abstract: Graph neural networks (GNNs) have become the prevailing methodology for addressing graph data-related tasks, permeating critical domains like recommendation systems and drug development. The ...
IEEE

Dynamic Graph Variational Sparse Coding for Power Distribution Network Fault Location and Classification

Abstract: Fault location and classification are crucial to the reliable and resilient operation of power distribution networks (PDNs). Current machine learning works cannot provide accurate and ...
GitHub

DeGEM: Out-of-Distribution Node Detection by Graph Energy-based Model

Official implementation for ICLR'25 paper ''Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs''[arxiv], by Yuhan Chen*, Yihong Luo*, Yifan Song, Pengwen ...
Frontiers

Resilience optimization and dynamic stability defense in active distribution networks under extreme disasters: a graph learning and cooperative control approach

1 Guangdong Power Grid Corporation Foshan Power Supply Bureau, Foshan, China 2 Electric Power Research Institute of China Southern Power Grid, Guangzhou, Guangdong, China Introduction: The escalating ...
GitHub

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems.

Official Code for the paper "Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems" (under submission, preprint available at arXiv ...