Dynamic graph algorithms and data structures represent a vital research frontier in computer science, underpinning applications from network analysis to real-time system monitoring.

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

practical hints for GNNs, relation to approximate graph isomorphism tests, R-convolution framework, graph kernels based on bag of structures and information diffusion, generative graph models: ...

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 ...

The enterprise knowledge graph is a knowledge representation system based on graph structures. It integrates multi-source data from both internal and external sources (such as business information, ...

Graph databases, including Neo4j and Amazon Neptune, are different from databases in that they are designed to facilitate the storage and querying of graph structures.

The rise of graph databases is closely related to AI's demand for data processing. AI technology requires vast amounts of structured and unstructured data, which must not only be input into ...

Alexander Christensen's recent study probably won't rewrite 40 years of history in the field of psychology, but he hopes that ...

Researchers uncovered new personality traits and developed a new personality hierarchy using novel data science methods in taxonomic graph analysis ...