Graph Neural Network

Graph Neural Networks (GNNs) are a class of machine learning models designed to analyze and learn from data represented as graphs, focusing on capturing relationships between nodes and their impact on downstream tasks like node classification and link prediction. Current research emphasizes improving GNN performance by addressing limitations such as oversmoothing and oversquashing through architectural innovations (e.g., incorporating residual connections, Cayley graph propagation) and novel training techniques (e.g., contrastive learning, Laplacian regularization). GNNs are proving valuable across diverse fields, including social network analysis, drug discovery, and financial modeling, offering powerful tools for analyzing complex relational data where traditional methods fall short.

Papers

December 27, 2023

December 25, 2023

Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks
Luis Carlos Rivera Monroy, Leonhard Rist, Martin Eberhardt, Christian Ostalecki, Andreas Bauer, Julio Vera, Katharina Breininger, Andreas Maier
Graph Neural Network Comparative Study Radiomics Feature Image Data Skin Cancer Classification Expression Profile

December 22, 2023

Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant
Sam Alexander Martino, João Morado, Chenghao Li, Zhenghao Lu, Edina Rosta
Graph Neural Network Side Chain Markov Chain Graph Partitioning Large Scale Training Kinetic Network

December 21, 2023

Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks
Gaël Gendron, Yang Chen, Mitchell Rogers, Yiping Liu, Mihailo Azhar, Shahrokh Heidari, David Arturo Soriano Valdez, Kobe Knowles, Padriac O'Leary, Simon Eyre, Michael Witbrock, Gillian Dobbie, Jiamou Liu, Patrice Delmas
Graph Neural Network Behavior Model Animal Behavior Complex Behavior Social Animal Causal Structure Discovery

December 20, 2023

December 19, 2023

December 18, 2023

December 17, 2023

Graph Neural Network

Papers

Performance Comparison of Session-based Recommendation Algorithms based on GNNs

FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNs

Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks

Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant

Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks

Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

NodeMixup: Tackling Under-Reaching for Graph Neural Networks

Fast Cell Library Characterization for Design Technology Co-Optimization Based on Graph Neural Networks

Towards Fair Graph Federated Learning via Incentive Mechanisms

When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook

Learning to Reweight for Graph Neural Network

Stronger Graph Transformer with Regularized Attention Scores

Learning Domain-Independent Heuristics for Grounded and Lifted Planning

Robust Node Representation Learning via Graph Variational Diffusion Networks

Non-Euclidean Spatial Graph Neural Network

p-Laplacian Adaptation for Generative Pre-trained Vision-Language Models

Enabling Accelerators for Graph Computing

Degree-based stratification of nodes in Graph Neural Networks

Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks

A charge-preserving method for solving graph neural diffusion networks