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

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...

Researchers have developed an artificial intelligence model that predicts crime more accurately than several existing ...

Spread the love“`html Understanding how to create a neural network can be a game-changer in the fields of artificial intelligence and machine learning. As industries increasingly rely on data-driven ...

Abstract: Dear Editor, This letter presents a novel graph neural network, namely modularized graph convolution network (MGCN), to address the underexplored issue in graph convolution networks (GCNs), ...

Abstract: A dynamic graph (DG) is commonly encountered in many big data-related application scenarios, like cryptocurrency transaction analysis. A dynamic graph convolutional network (GCN) can ...

--edge-path STR Input graph path. Default is `input/bitcoin_otc.csv`. --features-path STR Features path. Default is `input/bitcoin_otc.csv`. --embedding-path STR ...

Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as ...

eDepartment of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK fDepartment of Psychosis Studies, Institute of Psychiatry, Psychology ...

In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep ...

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 ...