The Neo4j ecosystem made graph data mainstream. Developers and business users alike can now easily structure their data in a way that matches the real-world.
This real-world view is especially useful for detecting fraud: who is involved, where and when does it take place, what assets do they hold, and - most importantly - how are each of these details connected.
Wikipedia’s community-based approach to editing makes it prone to many types of fraud, including artificial bot activities, brigading, digital vandalism and collusive behaviors. By examining controversial and topical Wikipedia articles together with their rich edit history, Christian demonstrates how using GRANDstack tooling the smart way can enable the detection of suspect patterns of behavior.
GRANDstack gives developers the premium tools they need to build consistent, extensible applications for trend and pattern analysis. Christian guides you through the steps needed to implement an end-to-end anomaly detection platform, explaining why each technology in the modern GRANDstack approach is the right choice. He’ll include:
- streaming techniques for ingesting large volume data
- core graph modeling in Neo4j
- GraphQL querying to reveal complex interaction behaviors
- visualizing and actioning suspicious networks in a React front-end
Christian has extensive Neo4j Graph platform experience. He’s worked with Fortune 500 companies building graph analytics applications and bringing them to production successfully.
