Trillion edges benchmark: new world record beyond 100TB by TigerGraph featuring AMD based Amazon EC2 instances
Graph databases have become increasingly popular in recent years, as they are uniquely suited to handle complex, interconnected data. As data sets continue to grow, scaling up graph databases to…
Transaction Surveillance with Maximum Flow Algorithm
In the banking, payment platforms, and cryptocurrency industries, graph analytic approaches such as PageRank, Label Propagation, and Cycle Detection have proven to be valuable for tracking abnormal transaction patterns, conducting…
Turbocharge your business intelligence with TigerGraph’s ML Workbench on TigerGraph Cloud
How to develop a graph-enhanced machine learning model using the most advanced graph analytics and graph ML with TigerGraph’s ML Workbench together with TigerGraph Cloud. The source code and examples…
Developer Spotlight: Kapil Saini
TigerGraph Developer Spotlights allow you to get to know people using TigerGraph around the world to create powerful connected data solutions answering countless use cases. Meet Kapil Saini Learn about…
What is a Graph Database and Why Should You Care?
The world is becoming more connected every day. Where have you heard that before? Enterprises, data analytics companies, data scientists … we’re all finding new ways to explore connections and relationships…
Graph Neural Network-based Graph Outlier Detection: A Brief Introduction
This blog is written by Yingtong Dou, a Ph.D. candidate at the University of Illinois Chicago, working on graph mining, fraud detection, and secure machine learning. The content of this…
The Future of AI and Machine Learning in Fraud Detection
This transcript is edited from the TigerGraph Connections podcast published on September 12, 2022, with TigerGraph’s Sebastian Aldeco.Corey Tomlinson: Tumultuous times lead inevitably and unfortunately to fraud, especially for large…
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