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Abstract: In light of the growing emphasis on the right to be forgotten of graph data, machine unlearning has been extended to unlearn the graph structures’ knowledge from graph neural networks (GNNs) ...

The primary purpose of this study is to present mathematical modeling methods inspired by graph theory operations and logic as a tool to structurally analyze the socio-economic composition of a city ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...

Abstract: Graph neural network is a new neural network model in recent years, whose advantage lies in processing graph structure data. In the era of big data, people can collect a large amount of ...

Spatial-temporal data handling involves the analysis of information gathered over time and space, often through sensors. Such data is crucial in pattern discovery and prediction. However, missing ...

The Graph, the decentralized indexing system that works much like Google for blockchains, has introduced a data standard for Web3. Called GRC-20, the standard would define how information is ...

Knowledge graphs are reshaping how we organize and make sense of information. By connecting data points and revealing relationships between them, these powerful tools are transforming industries, from ...

Ego-centric searches are essential in many applications, from financial fraud detection to social network research, because they concentrate on a single vertex and its immediate neighbors. These ...