10+ Entity States in Graph Visualisation and How To Make The Best Of Them
Dr. Miro Marchi and Michal Trnka explore 10 of the most useful graph entity states using Cypher to enrich entities with contextual information enabling powerful interactions.
Neo4j Security In Action
Christophe Willemsen describes Role Based Access Control (RBAC), intra-cluster encryption and logging in Neo4j.
Post-Union Processing with Cypher
Luanne Misquitta shows how to use the results of a UNION Cypher query.
Malt Aware: Discovering What To Drink With Neo4j
Luanne Misquitta explains how to produce good starting-point recommendations for whisky using Cypher that are of higher quality than those we see at our favourite online stores.
Knowledge Graphs Powered By NLP and Network Science
Vlasta Kůs takes us through converting a corpus of research papers through Natural Language Processing, entity (relation) extraction and graph algorithms to highly informative connected insights organized in a knowledge graph.
How Graph Databases Deliver Enterprise Grade Security
This presentation by Christophe Willemsen, CTO, GraphAware, guides you through security best practices for Neo4j development.
Improving Information Retrieval with Knowledge Graphs and Natural Language Processing
Christophe Willemsen, CTO, GraphAware, explains how to apply NLP to extract entities and key phrases to build and search knowledge graphs
Knowledge Graphs to Power Financial Chat Bots
Mayank Gupta, SVP of Data and Wren Chan, VP of Foundational Architecture and Innovation from LPL Financial present how they use GraphAware Hume and Neo4j to power financial chat bots.
European Space Agency case study
Hume maps a segment of the space and satellite ecosystem for the European Space Agency
‘The ability to customise Hume Actions via Cypher queries provided ESA with flexibility to cover a range of use cases and customers.’
Unparalleled Graph Database Scalability Delivered by Neo4j 4.0 - Graph Powered Machine Learning
Presentation by Dr. Alessandro Negro, Chief Scientist at GraphAware and author of the Manning’s book Graph-powered machine learning, that covers the following topics:
Why unlimited scale is important when using graph databases
The new graph database scaling capabilities built by Neo4j developers
The role of graphs to support machine learning application
How Neo4j assists customers in scaling their applications
Concrete examples of machine learning projects that can leverage graph sharding
The recording is available as well: https://bit.ly/39ZqFVE