New Oracle Magazine Article on Oracle Big Data Spatial & Graph for Social Network Analysis

Back at the beginning of 2016 I presented a session at the BIWA 2016 Conference on using Oracle’s (at that time) new Big Data Spatial and Graph product to do what’s called “social network analysis”; a form of graph analysis using the networks formed by social networks, in this case Twitter, to help understand who’s central to a particular network, who influences who, who are the best connectors through the network to someone you might want to speak to, and so on.

Twitter works well as a network to analyse for these types of presentations as most people are familiar with the tweets, replies, hashtags, retweets and other types of connections users can create with this social network, and Oracle Big Data Spatial and Graph helps you identify the communities that users not only self-declare through hashtags (for example, “#00w16”) but also through the connections they build up by just interacting with the same people over and over again — a graph analysis technique called “clustering” that identifies groups of users based purely on their common networks of communication.

If you’re interested in the topic and want to understand a bit more about how this type of graph analysis works, as well as see how this new Oracle Big Data product looks, the new September/October 2016 edition of Oracle Magazine has a follow-up article by myself on the topic called “Social Network Analysis : Use Oracle Big Data Spatial and Graph to analyze social networks” that uses some Twitter data I provide along with software you can download from OTN if you want to try it out yourself.

Once you’ve worked through the article, be sure to check out the official blog from the product team for more scripts and examples including product recommendations using the personalized page rank algorithm, and fraud detection in finance spotted by identifying circular payment relationships — two very good examples of data analysis that would be very hard to perform using traditional relational data models.