Author: Zhong Yangyang Reviewed by: Chen Zhiyan This article is approximately 2500 words long and is recommended to be read in 5 minutes. This article provides a brief introduction to the basic concepts of Graph Neural Networks and typical models. A graph (Graph) is a type of data structure that can naturally model the complex … 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
Interview Guest | Yang Hongxia Author | Cai Fangfang Editor | Chen Si AI Frontline Guide: Graph Neural Networks (GNN) have undoubtedly become the “new darling” of AI in 2019. However, due to the inherent complexity of training GNNs, supporting efficient and scalable parallel computation is very challenging. Currently, GNN platforms have also become a … Read more
A public academic platform initiated by overseas scholars Sharing information, integrating resources Exchanging academic ideas, occasionally discussing life In recent years, machine learning algorithms have made significant advancements in many fields, including natural language processing and image recognition. Thanks to the continuous improvement and development of material databases such as Materials Project1, QM92,3, machine learning … Read more
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
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
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
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
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
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