Source: DeepHub IMBA This article is approximately 2600 words long and is suggested to be read in 8 minutes. This article will delve into heterogeneous GNNs, which can handle different types of nodes and their unique features. Graph Neural Networks (GNNs) are powerful tools for predicting the behavior of complex systems, such as social networks, … Read more
Click on the above“Beginner’s Visual Learning”, choose to add Star or Pin. Essential content delivered to you first-hand. Basic Knowledge The Graph Convolutional Network (GCN) is a type of generalized neural network structure based on graph structures that has gained widespread attention and research from scholars in recent years due to its unique computational capabilities. … Read more
MLNLP(Machine Learning Algorithms and Natural Language Processing) is one of the largest natural language processing communities in China and abroad, gathering over 500,000 subscribers, covering domestic and international NLP master’s and doctoral students, university teachers, and corporate researchers.The vision of the community is to promote communication and progress between the academic and industrial sectors of … Read more
This article is about 8000 words long and is suggested to be read in 16 minutes. This article provides a detailed introduction to the relevant content from Graph to Graph Convolution. The author has recently reviewed several papers on Graphs and Graph Convolutional Neural Networks (GCNs) and is deeply impressed by their power. However, some … Read more
Graph Deep Learning (GDL) has been accelerating its development over the years. Many real-life problems have made GDL a universal tool, showing great potential in areas such as social media, drug discovery, chip implantation, prediction, and bioinformatics. This article will provide a detailed review and explanation of popular Graph Neural Networks (GNN) and their mathematical … Read more
There is considerable research on using Graph Neural Networks (GNN) for computer vision (CV), but it typically revolves around point cloud data, with few directly addressing image data. Compared to CNNs, which treat an image as a grid, and Transformers, which flatten images into sequences, graph methods are more suitable for learning features of irregular … Read more
PanChuang AI Share Source | Extreme City Platform Author | whistle@Zhihu Source | https://zhuanlan.zhihu.com/p/536489997 Reprinted from | Machine Learning Algorithms and Natural Language Processing Introduction Why Use Transformers on Graphs? Briefly mention the benefits brought by Graph Transformers (GT): Can capture long-range dependencies Mitigates over-smoothing and over-squashing phenomena GT can even integrate GNN and frequency … Read more
This article is about 10,000 words long, and it is recommended to read for over 10 minutes. This article introduces graph FMs and provides examples of their use. Foundation models in language, vision, and audio have become one of the main research topics in machine learning for 2024, while FMs targeting graph-structured data are somewhat … Read more
This article is approximately 9000 words long and is recommended for a reading time of over 10 minutes. This article analyzes the reliability risks of GNNs from a causal perspective and introduces six sets of techniques to gain deeper insights into potential causal mechanisms and achieve reliability. 1 Introduction This article reviews the latest advancements … Read more
Author: yyHaker Source: https://zhuanlan.zhihu.com/p/136521625 This article is about 5900 words long and is recommended to be read in 10 minutes. This article will provide a simple summary from a more intuitive perspective of the currently popular classic GNN networks, including GCN, GraphSAGE, GAT, GAE, and graph pooling strategies such as DiffPool. In recent years, the … Read more