Graph-Powered Analytics: Why You Need It and How to Learn It
Businesses need to stay ahead of the competition, to cut losses, and to find more revenue. By looking at their data as a network and then analyzing the connections –…
The Connected Customer: Building the Foundation with TigerGraph
In the rapidly evolving world of customer-facing businesses, providing an exceptional omnichannel customer experience has become the key to success. As online retail sales have soared over the last decade,…
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It’s Time to Harness the Power of Graph Technology [Infographic]
It’s time for your enterprise to harness the power of graph technology. A graph database platform – like TigerGraph – stores your data in intuitive, connected patterns so that queries…
Understanding Bank Fraud ~ by Harry Powell
Bank fraud is a serious concern that affects financial institutions and their customers worldwide. Large organized criminal groups are often the primary perpetrators of fraud, and understanding their tactics is…
How Is Graph Database Used to Combat Money Laundering? ~ by Mingxi Wu, CEO, TigerGraph
Figure 1: AML generic workflow. Financial accounts are linked to many transactions. Alert entities are suspicious accounts that are presented to fraud analysts, who can further put alert entities into…
TigerGraph Puts State-of-the-Art Graph Neural Networks within Reach
In a relatively short time, machine learning has completely transformed almost every facet of technology in the business world and in our day-to-day lives. From driving directions to automated stock…
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…
