As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

Pair Perplexity with Google’s NotebookLM for speedy research, with inline citations and presenter or detailed slide options, ...

Heterogeneous graphs organize data with nodes and edges, and have been widely used in various graph-centric applications.

Knowledge graphs and ontologies form the backbone of the Semantic Web by enabling the structured representation and interconnection of data across diverse domains. These frameworks allow for the ...

Community search has emerged as a fundamental task in the analysis of large-scale graphs and social networks, where the aim is to efficiently extract subgraphs that are both structurally cohesive and ...

Science and data are interwoven in many ways. The scientific method has lent a good part of its overall approach and practices to data-driven analytics, software development, and data science. Now ...

A super geeky topic, which could have super important repercussions in the real world. That description could very well fit anything from cold fusion to knowledge graphs, so a bit of unpacking is in ...

Dublin, Jan. 31, 2025 (GLOBE NEWSWIRE) -- The "Knowledge Graph Market by Solution (Enterprise Knowledge Graph Platform, Graph Database Engine, Knowledge Management Toolset), Model Type (Resource ...