TigerGraph

Step 1: Model accounts, transactions, and users as vertices and edges in a graph.

Step 2: Load historical transaction data into the graph.

Step 3: Define fraud patterns using GSQL queries.

Step 4: Execute queries to identify suspicious connections and flag potential fraud cases.

Step 1: Model customers, products, orders, and interactions as vertices and edges.

Step 2: Ingest data from CRM, e-commerce, and social media platforms.

Step 3: Use graph queries to identify customer preferences, purchase history, and social connections.

Step 4: Personalize recommendations and marketing campaigns based on customer insights.

Step 1: Model suppliers, manufacturers, distributors, and products as vertices and edges.

Step 2: Load data about inventory levels, lead times, and transportation routes.

Step 3: Use graph algorithms to identify critical paths and single points of failure.

Step 4: Optimize resource allocation and mitigate potential disruptions.

Step 1: Model network devices, users, and connections as vertices and edges.

Step 2: Ingest network traffic logs and security event data.

Step 3: Use graph queries to detect anomalous patterns and identify potential security breaches.

Step 4: Automate threat response and prevent further damage.

Step 1: Model entities and their attributes as vertices and edges.

Step 2: Load data from multiple data sources.

Step 3: Define similarity rules and use graph algorithms to identify potential matches.

Step 4: Merge duplicate records and create a unified view of each entity.