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
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
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
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
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
Originally from Python Artificial Intelligence Frontier Graph Neural Networks are widely used in recommendation systems, knowledge graphs, and traffic road analysis due to their advantages in processing non-Euclidean space data and complex features. However, when the graph data volume increases, problems arise: computation becomes extremely slow, memory cannot hold it, and communication in distributed systems … Read more
Click on the above“Visual Learning for Beginners”, select to add a star or “pin” Heavyweight insights delivered in real-time Author: Compiled by: ronghuaiyang Introduction The aim of this perspective is to build intuition behind the Transformer architecture in NLP and its connection to Graph Neural Networks. Engineer friends often ask me: “Graph deep learning” sounds … Read more
Resource Sharing Edited by: Rabbit Unknowingly, 2019 is about to pass Time flies, and you have accompanied us for another year This year We have compiled 33 papers and 165 news updates Prepared 46 paper interpretations Shared 38 pieces of technical content Held 6 sessions of AI Talking And organized 1 offline salon called “Graph … Read more
Background Introduction The main purpose of Graph Representation Learning (Graph Representation Learning, Network Embedding, Graph Embedding) is to map each node in the graph to a low-dimensional vector space, preserving the original structure and distance information of the graph. Intuitively, two points that are close together in the graph should also be close together in … Read more
Hello everyone, I am Xiao Wu Ge. It has been a while since my last update. Recently, I have been looking at various topics, and I don’t know where to start. I have been quite busy with work, but I managed to find some time this weekend to write an article. Most of the articles … Read more