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 7, 2023

December 6, 2023

December 5, 2023

December 4, 2023

December 3, 2023

Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural Networks
Zheyi Qin, Randy Paffenroth, Anura P. Jayasumana
Graph Neural Network Graph Based Learning Coordinate Based Coordinate Network Conventional Neural Network Topological Neural Network

December 2, 2023

Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design
Tom McDonald, Calvin Tsay, Artur M. Schweidtmann, Neil Yorke-Smith
Graph Neural Network Mixed Integer Molecular Design Global Optimality

November 30, 2023

Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems
Hongjian Gu, Yaochen Hu, Yingxue Zhang
Graph Neural Network Generative Model Recommender System Preference Feedback Interaction Graph Based Collaborative Filtering

November 29, 2023

Graph Neural Network

Papers

GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts

A Structural-Clustering Based Active Learning for Graph Neural Networks

Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks

Adaptive Dependency Learning Graph Neural Networks

Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs

Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non-Linear PA Distortion

On the Initialization of Graph Neural Networks

NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams

Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks

On the Trade-Off between Stability and Representational Capacity in Graph Neural Networks

LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks

A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data

The Self-Loop Paradox: Investigating the Impact of Self-Loops on Graph Neural Networks

Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians

Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural Networks

Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design

Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems

Enhancing Data-Assimilation in CFD using Graph Neural Networks

Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs

GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs