Resources
Unparalleled Graph Database Scalability Delivered by Neo4j 4.0
This session features Dr. Alessandro Negro, noted graph database author and Chief Scientist at GraphAware, along with Patrick Wall, Director of Product Marketing at Neo4j. During this webinar, GraphAware explores the powerful scalability features of Neo4j 4.0 in a live demo using the COVID-19 Open Research Dataset.
Graph-Powered Machine Learning - Book
At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.
Knowledge Graphs in Action
In this presentation, you’ll learn how companies are building Knowledge Graphs with Neo4j and the Hume platform in order to surface previously undiscoverable insights. We’ll go over the process of analysing unstructured data using Machine Learning techniques and how graphs are a wonderful representation for storing Knowledge, making it naturally connectable. Lastly, a Graph Visualisation demonstration will take place, showing new insights discovered from the results of the previous operations.
Graph Hyper-Growth Ahead: 5-Minute Interview with Kyle McNamara
Interview with Kyle McNamara, CEO, Americas at GraphAware, about how GraphAware works alongside Neo4j (conducted at GraphTour DC 2019)
To Be or Not To Be, Neo4j Full Text Search Tips and Tricks
Christophe Willemsen, CTO at GraphAware, goes over some tips and tricks on Relevant Search with Neo4j’s Lucene based search engine.
Social media monitoring with ML-powered Knowledge Graph
Ever wondered how ML can be used to build a Knowledge Graph to allow businesses to successfully differentiate and compete today? We will demonstrate how Computer Vision, NLP/U, knowledge enrichment and graph-native algorithms fit together to build powerful insights from various unstructured data sources.
It Depends (and why it’s the most frequent answer to modelling questions)
The answer to most general purpose graph modelling questions is “it depends”. This talk demonstrates the pitfalls of modelling without knowing use cases- it shows how two sets of people can produce two different models for the same set of data elements, and how use cases should guide the model.