Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

Dr. Alin Deutsch of UC San Diego explains in a Q&A why graph database algorithms will become the driving force behind the next generation of AI and machine learning apps.

Knowledge graphs are well-suited to organizations with large data sets and where extracting knowledge often proves burdensome. Newsletters Games Share a News Tip Featured ...

LinkedIn, for example, uses a knowledge graph to structure and interconnect data about its members, jobs, titles, and other entities. It uses its knowledge graph to enhance its recommendation ...

For example, a user might input a question like “Which policies have a high-risk rating?” and the LLM can generate a Cypher query to extract the relevant data from the graph.

To illustrate the kinds of relationship that a hypergraph can tease out of a big data set — and an ordinary graph can’t — Purvine points to a simple example close to home, the world of scientific ...

Available starting today, Neo4j Aura Graph Analytics is said to work with any kind of data source, including Oracle, Microsoft SQL, Databricks, Google BigQuery, Snowflake and Microsoft OneLake.

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 ...