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 ...

This project implements a custom Graph Data Structure in Java to solve two real-world problems involving pathfinding. It avoids external libraries and uses only core Java logic for BFS/DFS-based ...

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 ...

The timely and accurate prediction of maize (Zea mays L.) yields prior to harvest is critical for food security and agricultural policy development. Currently, many researchers are using machine ...

Data structure for dynamic connectivity in undirected graphs. Supports adding and removing edges and checking whether two vertices are connected (there's a path between them) in polylogarithmic time.