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Tuesday, June 5 • 1:15pm - 2:00pm
(want: Recommendations) -[:WITHOUT] -> (:FalsePositives)

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It takes a graph to build a community from data.  Identifying communities and their traits is the critical to building effective recommendation engines.  And recommendation engines come in many forms, including chatbots, fraud detection, failure impact analysis, network analysis, genomic research, and an almost endless list of other applicable use cases. The application of community detection algorithms requires a graph database technology that can model the complexity of the real world and execute those algorithms at scale. Traditional technologies for community detection are often simplistic, hard to model a query path for, and in the worst case, error prone (e.g. false positives).

This session will cover:

  1. The core capabilities of a graph database and how it differentiates from other data stores, such as RDBMS’s.
  2. An example of how knowledge and inference can be represented and leveraged using a graph technology.
  3. Using graph algorithms to detect communities in the graph to drive applications, such as recommendation engines.

The session will use the Cypher query language, originally developed by Neo4j, for any examples shown. You've already been exposed to Cypher if you've read this far.

avatar for Dan Flavin

Dan Flavin

Sr. System Engineer, Neo4j
Dan is a Chicago based pre-sales engineer with an extensive experience in all things data. Over the last 10+ years, Dan has worked with clients to envision and successfully implement solutions in the Hadoop, NoSQL, Machine Learning, and Massively Parallel Database technology realms... Read More →

Tuesday June 5, 2018 1:15pm - 2:00pm
Garden Room (Kopp A-B-C) Normandale Partnership Center, 9700 France Ave So, Bloomington, MN 55431

Attendees (31)