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 ...

Imbalanced data classification is a challenging task in real applications. In this work. A method is proposed for image classification using imbalanced distribution of classes. The proposed method ...

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 ...

In the modern workplace, data is spread across numerous platforms and services, creating a need for a unified way to access and interact with this data. Microsoft Graph API serves as a bridge that ...

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 ...