Idiro’s social network analytics platform relies on complex processing to summarize, build and evaluate the behaviour of individuals through their communication patterns and observed actions. The principal challenge faced by Idiro in performing analysis is the sheer size of the input data. Social networks comprising of many hundreds of millions of actors are not unknown, with each actor often having dozens or links that together form complex large-scale communication graphs. Recording, parsing and evaluating these graphs would be time-consuming using current off-the-shelf data mining tools, and would likely become infeasible given the processing and memory limitations of even current enterprise-grade servers.

To overcome the challenge of scale, Idiro uses an Hadoop-based distributed processing environment to process even the largest social networks. We have developed a number of bespoke data mining tools and algorithms specific for social network analysis and leverage cluster-based processing to increase processing throughput. Similarly, Idiro provides customers with the option of using multiple graph-partitioning, clustering, link-traversal and community-specific predictive models, suited to particular domains and for specific needs.

Today, the analysis of social networks containing many millions of actors has become possible, enabling Idiro to predict the behaviour of individuals within the context of a much larger group. In addition, using complex graph partitioning algorithms, we can break a very large network into communities of closely linked individuals, such as friends, business colleagues and families.