As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

The convergence of graph theory, social network analysis and multimedia data mining has emerged as a vibrant interdisciplinary field. Graph models provide a structured representation of interactions ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...

Graph machine learning (or graph model), represented by graph neural networks, employs machine learning (especially deep learning) to graph data and is an important research direction in the ...

Graph databases excel for apps that explore many-to-many relationships, such as recommendation systems. Let’s look at an example Jeff Carpenter is a technical evangelist at DataStax. There has been a ...

The world's only multi-model graph database combining relational (PostgreSQL) and graph model Enterprise graph database that integrates legacy data environment Raising $10 million for AgensGraph ...

Polyglot persistence is becoming the norm in big data. Gone are the days when relational databases were the one store to rule them all; now the notion of using stores with data models that best align ...

Graph databases explicitly express the connections between nodes, and are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. There has ...