Introduction to Graph Analytics and Graph Database

Here is an overview of graph analytics and graph database.
According to industry analysts, graph technology will be foundational for 80% of Analytics and ML workloads by 2025. The reason for this explosion in growth is organizations need to ask more and more complex questions of their ever-growing data sets. Furthermore, data science professionals are realizing that the connections, or relationships, that tie their data together are just as important as the data points themselves. With graphs, those relationships can be treated as first-class data citizens - eliminating the need for time-consuming and computationally expensive joins.
In this session, we’ll cover a high-level overview of GraphDB, what makes them different, and what use cases graph technology is optimal for. From there, we’ll dive into the specific use case of fraud detection and run a demo of what a potential graph-based solution could look like. This talk will help you understand how graph analytics is being used today by some of the world’s most innovative organizations.
By the end of the session, you will have an understanding of the following:
- What makes graphs different
- What use cases are a good fit for graphs
- Why graph is a value add to current ML approaches
- How you can begin to leverage graphs today
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Presentation slides: info.datasciencedojo.com/hubf...
Table of Contents:
00:00 Introduction to Graph Analytics
08:14 Machine Learning and Algorithm Support
11:26 Common Use Cases
12:46 Q&A Break
18:20 Focus on Fraud
23:20 Fraud Detection in Financial Services
32:47 Q&A Break
54:12 Get in touch with us!
54:15 Resources
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#graphanalytics #graphdatabase #graphdb

[@Datasciencedojo]

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[@lavinadurgani3149]

Thank you for such an easy to follow video on Graph databases and analytics, and TigerGraph capabilities

[@prof_math_raid5567]

Interested