S&P 500 earnings surged 60% in 3 years—dot-com bust fears? See why cash flows, capex discipline, and stricter accounting may ...

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...

Abstract: Spectral graph convolutional networks (SGCNs) are one of the leading tools to handle learning tasks with graph structure. SGCNs leverage graph structure to define the graph spectral ...

This repository hosts the open sources of the Neo4j Graph Data Science (GDS) library. The GDS library is a plugin for the Neo4j graph database. GDS comprises graph algorithms, graph transformations, ...

👉 Learn how to graph exponential functions involving vertical shift. An exponential function is a function that increases rapidly as the value of x increases. To graph an exponential function, it is ...

Retrieval-augmented generation (RAG) has emerged as a pivotal framework in AI, significantly enhancing the accuracy and relevance of responses generated by large language models (LLMs) leveraging ...

This code was tested with PyTorch 2.0.1, cuda 11.8 and torch_geometrics 2.3.1. Note that ${PROJECT_DIR} refers to this directory. The following section outlines the ...

Abstract: The rapid development of Knowledge Graph (KG) technology has led to the emergence of Temporal Knowledge Graphs (TKGs), which hold significant research importance and value. Temporal ...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...