ETA Prediction with Graph Neural Networks in Google Maps - Paper Explained
Explore Graph Neural Networks for ETA prediction in Google Maps, covering graph formation, features, GNN architecture, loss functions, baselines, inference, and experimental results.
Graph Neural Networks- A Gentle Introduction
Gentle introduction to Graph Neural Networks, covering key concepts, properties, and variants. Learn about graph representation, information propagation, and common tasks in this emerging field of machine learning.
Graph Convolutional Network Paper Explained
Comprehensive explanation of Graph Convolutional Networks (GCNs), covering theory, derivation, and practical applications in node classification, with insights into their functionality and effectiveness.
How to Get Started With Graph ML - Blog Walkthrough
Comprehensive introduction to Graph Machine Learning, covering applications, methods, and resources. Explores research challenges, GNN expressivity, and related subfields, providing a solid foundation for beginners.
Graph Attention Network Project Walkthrough
Comprehensive walkthrough of Graph Attention Network implementation, covering dataset analysis, key implementation details, and related deep learning projects for enthusiasts and practitioners.
Temporal Graph Networks - GNN Paper Explained
Explore temporal graph networks and dynamic graphs, learning advanced techniques for graph machine learning, including time-based sampling, memory management, and attention mechanisms.
Graph SAGE - Inductive Representation Learning on Large Graphs - GNN Paper Explained
Deep dive into Graph SAGE, exploring its innovative approach to large-scale graph learning. Covers key concepts, training methods, aggregator functions, and comparisons with other graph neural networks.
Graph Convolutional Networks - GNN Paper Explained
Comprehensive exploration of Graph Convolutional Networks, covering theory, implementation, and applications. Delves into spectral methods, Weisfeiler-Lehman perspective, and GNN depth, offering insights for both beginners and experts.
Graph Attention Networks - GNN Paper Explained
Explore Graph Attention Networks (GAT) in-depth, covering graph theory basics, GAT methodology, multi-head versions, visualizations, and applications in transductive/inductive learning scenarios.
Graph Neural Networks Implementation in Python
Learn Graph Neural Networks implementation in Python, covering graph representations, node embeddings, message passing, and practical applications using NetworkX and PyG libraries.