Sr. Data Scientist at a computer software company with 1,001-5,000 employees
Real User
Top 10
Jun 27, 2026
I have been using TigerGraph for roughly seven years, and I was one of the first people in the first batch of cohorts to get certified in TigerGraph. My main use case for TigerGraph is for fraud detection and for looking at similarities in data sets. In my current job, when I worked within the finance space for certain banks such as Capital One and Washington Mutual Bank, I used TigerGraph for fraud detection. Most currently in my consulting work, I use a mix of graph databases, including TigerGraph, primarily for classification of metadata for models to adhere to compliance policies, such as the European Artificial Intelligence Act. The main area I use those multi-modal capabilities for is fraud detection, but I also use TigerGraph for metadata and data model cards. Most recently, I worked for a travel company where I used TigerGraph for classifying transactions for hotel data and identifying commonalities for sales data, which helped tailor customized marketing to customers based on their travel destinations.
I mainly use TigerGraph for enabling people on the migration of projects where they are using legacy systems and want to benefit from using a graph database, particularly for establishing recommendation engines for their different product-related information and relationships between the products, allowing them to recommend various products to end users. We usually conduct sessions specifically like SME connects, where we walk teams through TigerGraph's features, the GSQL query language, various algorithms, and specific use cases that can help them with their projects. This full-fledged training delivery also enables certification and practical application for their individual projects. We have deployed TigerGraph in the public cloud, specifically using TigerGraph Savana for our application.
I have been using TigerGraph for the last two years. The use case for TigerGraph is at quran.com, where we need to connect the mapping verse connections to model each verse as a node and the relationship between the verses, such as shared themes, concepts, and linguistic links. This creates a semantic network of the Quran, and I made a thematic visualization of the Quran for this structure. First, I needed to define what kind of nodes and relationships our graph would contain using TigerGraph. For the verse, I made it Surah, including Surah number, verse number, Arabic text, and translation. Then I created a theme with the primary ID and the description. I created a unidirectional edge called has_theme from verse to theme, and then I created a unidirectional edge called related_to from verse to verse with relationship type, which could be a string such as same_theme, cities, or explains, and then the weight as a double. In this schema, each verse is a vertex, each theme is a vertex, and vertices connect to themes through has_theme edges. Verses can also connect directly to other verses through related_to edges where they share semantic connections. I inserted data for verses sharing the same theme. I then wrote a query to find verses by theme using the TigerGraph GSQL query language to retrieve all verses connected to a specific theme. The visualization graph shows two circular nodes representing the verses, labeled such as 2:1, 3, and 3:200, and one center node representing the theme patient. Edges shown as arrows connect each verse to the patient node and also a direct edge between the two verses if the related_to connection was added.
Senior DB Engineer And Sre at a tech vendor with 10,001+ employees
Real User
Top 20
May 14, 2026
My main use cases for TigerGraph include a knowledge base, which is the number one use case, and second is fraud detection analysis. A specific example of how I use TigerGraph for fraud detection relates to cybersecurity or threat detection, which is very relevant to our infrastructure. It can identify the model, the host, users, permissions, logging patterns, and network connections. It can detect any kind of insider threat detection, attack path analysis, and lateral movement detection. It can find all the systems reachable from one compromised account, enabling threat detection or fraud detection.
Responsable Del Equipo D Más D at a tech services company with 51-200 employees
Real User
Top 10
May 12, 2026
My main use case for TigerGraph is a digital twin of the supply chain. I put all the data from companies' supply chains into it, model them as a graph, and in this way, link all the relationships between all the nodes. I am able to simulate what will happen in the supply chain based on the information from the graph. I also use TigerGraph for Graph RAG in different language model applications.A specific example of how the digital twin has benefited my organization is that I have managed to make the digital twin work in near real time, carrying out scenario simulations that in traditional databases take several minutes or tens of minutes to run the queries. In TigerGraph, it is a matter of seconds. I have been able to show clients how the digital twin is capable of working in near real time by simulating scenarios of their supply chain.
TigerGraph serves as a graph database to model accounts and transactions as edges for our company. In our organization, we implement TigerGraph in an application called Hi HQ. We deal with a large amount of interconnected data, such as customer, transaction, and product relations, so we needed to implement a solution that can effectively analyze highly connected data. We modeled entities like customers, products, and transactions as nodes, and their interactions as edges. Using this, we built graph analytics workflows to traverse the relationships quickly and identify patterns such as suspicious activity and customer behavior trends.
TigerGraph offers a graph analytics platform that efficiently handles large-scale and complex data relationships, providing insights for informed decision-making.Specialized for big data, TigerGraph leverages a native parallel graph architecture to analyze data relationships rapidly. It is designed to manage extensive datasets, providing real-time insights that are invaluable for sectors like financial services, healthcare, and telecommunications. With its scalable infrastructure, it supports...
I have been using TigerGraph for roughly seven years, and I was one of the first people in the first batch of cohorts to get certified in TigerGraph. My main use case for TigerGraph is for fraud detection and for looking at similarities in data sets. In my current job, when I worked within the finance space for certain banks such as Capital One and Washington Mutual Bank, I used TigerGraph for fraud detection. Most currently in my consulting work, I use a mix of graph databases, including TigerGraph, primarily for classification of metadata for models to adhere to compliance policies, such as the European Artificial Intelligence Act. The main area I use those multi-modal capabilities for is fraud detection, but I also use TigerGraph for metadata and data model cards. Most recently, I worked for a travel company where I used TigerGraph for classifying transactions for hotel data and identifying commonalities for sales data, which helped tailor customized marketing to customers based on their travel destinations.
I mainly use TigerGraph for enabling people on the migration of projects where they are using legacy systems and want to benefit from using a graph database, particularly for establishing recommendation engines for their different product-related information and relationships between the products, allowing them to recommend various products to end users. We usually conduct sessions specifically like SME connects, where we walk teams through TigerGraph's features, the GSQL query language, various algorithms, and specific use cases that can help them with their projects. This full-fledged training delivery also enables certification and practical application for their individual projects. We have deployed TigerGraph in the public cloud, specifically using TigerGraph Savana for our application.
I have been using TigerGraph for the last two years. The use case for TigerGraph is at quran.com, where we need to connect the mapping verse connections to model each verse as a node and the relationship between the verses, such as shared themes, concepts, and linguistic links. This creates a semantic network of the Quran, and I made a thematic visualization of the Quran for this structure. First, I needed to define what kind of nodes and relationships our graph would contain using TigerGraph. For the verse, I made it Surah, including Surah number, verse number, Arabic text, and translation. Then I created a theme with the primary ID and the description. I created a unidirectional edge called has_theme from verse to theme, and then I created a unidirectional edge called related_to from verse to verse with relationship type, which could be a string such as same_theme, cities, or explains, and then the weight as a double. In this schema, each verse is a vertex, each theme is a vertex, and vertices connect to themes through has_theme edges. Verses can also connect directly to other verses through related_to edges where they share semantic connections. I inserted data for verses sharing the same theme. I then wrote a query to find verses by theme using the TigerGraph GSQL query language to retrieve all verses connected to a specific theme. The visualization graph shows two circular nodes representing the verses, labeled such as 2:1, 3, and 3:200, and one center node representing the theme patient. Edges shown as arrows connect each verse to the patient node and also a direct edge between the two verses if the related_to connection was added.
My main use cases for TigerGraph include a knowledge base, which is the number one use case, and second is fraud detection analysis. A specific example of how I use TigerGraph for fraud detection relates to cybersecurity or threat detection, which is very relevant to our infrastructure. It can identify the model, the host, users, permissions, logging patterns, and network connections. It can detect any kind of insider threat detection, attack path analysis, and lateral movement detection. It can find all the systems reachable from one compromised account, enabling threat detection or fraud detection.
My main use case for TigerGraph is a digital twin of the supply chain. I put all the data from companies' supply chains into it, model them as a graph, and in this way, link all the relationships between all the nodes. I am able to simulate what will happen in the supply chain based on the information from the graph. I also use TigerGraph for Graph RAG in different language model applications.A specific example of how the digital twin has benefited my organization is that I have managed to make the digital twin work in near real time, carrying out scenario simulations that in traditional databases take several minutes or tens of minutes to run the queries. In TigerGraph, it is a matter of seconds. I have been able to show clients how the digital twin is capable of working in near real time by simulating scenarios of their supply chain.
TigerGraph serves as a graph database to model accounts and transactions as edges for our company. In our organization, we implement TigerGraph in an application called Hi HQ. We deal with a large amount of interconnected data, such as customer, transaction, and product relations, so we needed to implement a solution that can effectively analyze highly connected data. We modeled entities like customers, products, and transactions as nodes, and their interactions as edges. Using this, we built graph analytics workflows to traverse the relationships quickly and identify patterns such as suspicious activity and customer behavior trends.