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 ...
GitHub

hkuds/lightgcl

This is the PyTorch implementation for LightGCL proposed in the paper LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation, International Conference on Learning Representation, ...
IEEE

Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease Using EEG Data

Abstract: Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as ...
GitHub

Network Analysis Made Simple

Welcome to the GitHub repository for Network Analysis Made Simple! This is a tutorial designed to teach you the basic and practical aspects of graph theory. It has been presented at multiple ...
C&EN

Simple-to-Apply Wetting Model to Predict Thermodynamically Stable and Metastable Contact Angles on Textured/Rough/Patterned Surfaces

‡ Zuckerberg Institute for Water Research, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 84990 Midreshet Ben-Gurion, Israel ∥ Water ...