Technical Column Author: Liu Zhongyu Edited by Luobotu Today, I want to share with you about Graph Convolutional Networks. With the development of artificial intelligence, many people have heard of concepts like machine learning, deep learning, and convolutional neural networks. However, Graph Convolutional Networks are not often mentioned. So, what are Graph Convolutional Networks? Simply … Read more
How to Define the Input and Output Schema of a Graph By default, the StateGraph operates using a single schema, and all nodes should communicate using that schema. However, different input and output schemas can also be defined for the graph. When different schemas are specified, the internal schema will still be used for communication … Read more
A very impressive RAG system introduction. For more resources, please visit: I’m currently discussing interesting topics with friends in the “Large Model Pit Avoidance Guide,” would you like to join? [Code Repository] GitHub
The development of Knowledge Graphs (KG) is closely linked to the advancements in Artificial Intelligence (AI) agents. Starting from their static origins, knowledge graphs have evolved to include dynamic, temporal, and event-driven paradigms, each unlocking new capabilities for AI systems. This article explores their evolution and how Large Language Models (LLM) integrate into these advancements. … Read more
Spatio-temporal data is the foundation of many intelligent applications, revealing the causal relationships between measurements at a specific location and historical data from the same or other locations. In this context, Adaptive Spatio-Temporal Graph Neural Networks (ASTGNNs) have emerged as a powerful tool for modeling these dependencies, particularly through data-driven approaches rather than predefined spatial … Read more