An On-Demand Webinar
Recorded on May 19, 2015
A common impediment to the analysis of large networks is the difficulty of determining risk relative to particular actors within the network. Often an investigator may be focused on just a handful of actors within the larger network, but assessing importance relative to this subset is difficult to do using traditional metrics. For example, a financial institution may be focused on transactions into and out of a particular customer’s accounts or a law enforcement agency may be interested in the strongest connections relative to the target of an investigation.
Hear Novetta discuss advanced analytical techniques for overcoming these challenges. Using insights from the field of social networking analysis in addition to an in-depth understanding of the challenges faced by the investigator, entities can be prioritized for investigation. This network-centric approach assigns risk based on an assessment of an entity’s characteristics and activity using an eigenvector centrality algorithm (of which Google’s PageRank algorithm is one application). Using open source tools, such as Python and R, we will demonstrate how this algorithm is able to help an investigator understand and prioritize a large network of actors.
Matt Teschke, Senior Quantitative Consultant, Novetta