TigerGraph Cloud Starter Kits

TigerGraph Cloud Starter Kits

TigerGraph Cloud Starter Kits are built with sample graph data schema, dataset, and queries focused on a specific use case such as Fraud Detection, Recommendation Engine, Supply Chain Analysis and/or a specific industry such as healthcare, pharmaceutical or financial services.

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Starter Kit





No pre-populated schema, dataset or queries

Customer 360 – Attribution and Engagement Graph

Create a real-time 360-degree view of the customer journey for attribution and engagement insights

COVID-19 Analysis

Detect hubs of infection and track the movements of potential spreaders

Cybersecurity Threat Detection-IT

Block cybersecurity threats by detecting interconnected events, devices and people

Enterprise Knowledge Graph (Corporate Data)

Analysis of corporate data including investors and key stakeholders

Enterprise Knowledge Graph (Crunchbase)

Knowledge Graph example featuring Crunchbase data with startups, founders and companies

Entity Resolution (MDM)

Identify, link and merge entities such as customers with analysis of attributes and relationships

Financial Services (Payments) – Fraud Detection

Detect and stop fraudulent payments in real-time

Fraud and Money Laundering Detection (Fin. Services)

Multiple types of fraud and money laundering patterns

GSQL 101

Introduction to TigerGraph’s powerful graph query language

Graph Analytics - Centrality Algorithms 

Determine the entity in your network that is most central to the others

Graph Analytics - Community Detection Algorithms

Find communities of a specific type in your network
Tags: Graph Algorithms

Graph Analytics - Shortest Path Algorithms

Identify the path through your network with the fewest number of hops

Healthcare Graph (Drug Interaction/FAERS)

Healthcare example focused on public (FAERS) and private data for pharmaceutical drugs

Healthcare – Referral Networks, Hub(PageRank) & Community Detection

Analyze member (patient) claims to establish referral networks, identify most influential prescribers (doctors) and discover the connected prescriber communities

Machine Learning and Real-time Fraud Detection

Mobile industry example for detecting fraud in real-time and generating graph-based features for training the machine learning solution

In-Database Machine Learning Recommendation 

Provide content and products suggestions using an in-database machine learning recommendation system

In-Database Machine Learning for Big Data Entity Resolution 

Match, link and group entities for creating a single identity across large datasets with in-database machine learning.

Low-Rank Approximation Machine Learning

Implement the low-rank approximation algorithm natively in-database to deliver personalized recommendations 

Network and IT Resource Optimization

Network and IT resource graph for modeling and analyzing the impact of the hardware outage on workloads

Recommendation Engine (Movie Recommendation)

Graph-based movie recommendation engine built with public data

Recommendation Engine 2.0 (Hyper-Personalized Marketing)

Hyper-personalized recommendation engine to create dynamic offers in real-time for higher click-through and revenue

Social Network Analysis

Social network example for understanding and analyzing relationships

Supply Chain Analysis

Example covering inventory planning and impact analysis