Graph neural networks (GNNs) have emerged as a versatile class of machine-learning models designed to process data structured as graphs, capturing relationships among entities through iterative ...

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 ...

Explore the concept of graph databases, their use cases, benefits, drawbacks, and popular tools. A graph database is a dynamic database management system uniquely structured to manage complex and ...

CrowdStrike CRWD recently unveiled its latest graph database — Asset Graph — that dynamically monitors and tracks complex interactions among assets providing graphic visualizations of the asset ...

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Graph databases represent one of the fastest-growing areas in the database market. MarketsandMarkets’ report on graph databases predicts that graph databases will grow from $1.9 billion in 2021 to ...

At some point in time, all users of social networking applications such as Facebook or LinkedIn have been pleasantly surprised at seeing an old school friend, or an ex-colleague from that first job, ...

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may ...

Victor Lee is director of product management at TigerGraph. Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both ...