Graph Neural Networks

From Knowledge Graphs to Cognitive Graphs: History, Development, and Prospects

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From Knowledge Graphs to Cognitive Graphs: History, Development, and Prospects

The research hotspot of knowledge graphs has gradually shown a tendency to emphasize quantity over structuredness, which is closely related to the prevalence of deep learning and connectionist ideas. Cognitive graphs dynamically construct knowledge graphs with contextual information based on the dual-processing theory of human cognition and perform reasoning. This article reviews the historical development … Read more

Practical Guide to Graph Neural Networks in TensorFlow

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Practical Guide to Graph Neural Networks in TensorFlow

Source: ZHUAN ZHI This article serves as a tutorial and is recommended for a 5-minute read. This tutorial's practical section will be based on TF-GNN, a library for handling graph-structured data in TensorFlow. Graphs are universal data structures that can represent information from various domains (social, biomedical, online transactions, etc.). Graph Neural Networks (GNNs) are … Read more

An Introduction to PyTorch Geometric for Graph Neural Networks

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Hello everyone, I’m Cat Brother! Today let’s talk about PyTorch Geometric, abbreviated as PyG. This is a library based on PyTorch specifically designed for handling Graph Neural Networks (GNN). If you’re interested in graph data such as social networks, recommendation systems, molecular structures, etc., then PyG is an excellent choice! PyG provides many efficient operations … Read more

Cutting-Edge Review: Multimodal Graph Learning for Complex System Modeling

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Cutting-Edge Review: Multimodal Graph Learning for Complex System Modeling

Introduction Graph Learning is a machine learning method that studies and applies graph-structured data. In graph learning, data is represented as a graph consisting of nodes and edges, where nodes represent entities or objects, and edges represent the relationships or connections between them. Therefore, graph learning is particularly suitable for multi-scale analysis, modeling, and simulation … Read more

Overview of Graph Neural Networks: Dynamic Graphs

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Overview of Graph Neural Networks: Dynamic Graphs

Introduction Graph neural networks (GNNs) have been widely applied to the modeling and representation learning of graph-structured data. However, mainstream research has been limited to handling static network data, while real complex networks often undergo structural and property evolution over time. The team led by Katarzyna at the University of Technology Sydney recently published a … Read more

Don’t Overlook Graph Neural Networks (GNN) in 2023!

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Don't Overlook Graph Neural Networks (GNN) in 2023!

Introduction Graph Neural Networks (GNN) — a dark horse among various neural networks. It is widely applicable across various fields, including recommendation systems, Google Maps traffic prediction, drug discovery, protein discovery, and more. To explore the development and real-world applications of graph neural networks in algorithmic neural solving, the Intelligence Club, in collaboration with Associate … Read more

A Complete Interpretation: What Makes Neural Networks Graph Neural Networks?

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A Complete Interpretation: What Makes Neural Networks Graph Neural Networks?

Click the above“Beginner Learning Vision” to select “Star” or “Pin” Heavyweight content delivered first-hand Introduction In recent years, there has been increasing interest in extending deep learning methods to graphs. Driven by multiple factors, researchers have drawn on ideas from convolutional networks, recurrent networks, and deep autoencoders to define and design neural network structures for … Read more

Principles and Applications of Graph Neural Networks (GNN)

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Principles and Applications of Graph Neural Networks (GNN)

This article is about 3200 words long and suggests a reading time of 6 minutes. Graph Neural Networks (GNN) are a type of deep learning method particularly adept at handling data with a graph structure. Graph Neural Networks (GNN) are a type of deep learning method particularly good at handling data with a graph structure. … Read more

Summary of Mathematical Principles Behind Graph Neural Networks

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Summary of Mathematical Principles Behind Graph Neural Networks

This article provides a detailed overview and explanation of popular Graph Neural Networks (GNNs) and their mathematical nuances. The underlying idea of graph deep learning is to learn the structural and spatial features of graphs with nodes and edges, where these nodes and edges represent entities and their interactions. >>> Original Link: https://rish-16.github.io/posts/gnn-math/ Graph Deep … Read more

A Summary of Graph Neural Networks

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A Summary of Graph Neural Networks

In recent years, the enthusiasm for research on Graph Neural Networks (GNNs) in the field of deep learning has been growing steadily, making GNN a hot research topic at major deep learning conferences. The remarkable ability of GNNs to handle unstructured data has led to breakthroughs in network data analysis, recommendation systems, physical modeling, natural … Read more