Pattern Matching Trajectories for Investigative Graph Searches

Abstract

Investigative graph search is the process of searching for and prioritizing entities of interest that may exhibit part or all of a pattern of attributes or connections for a latent behavior. In this work we formulate a related sub-problem of determining the pattern matching trajectories of such entities. The goal is to not only provide analysts with the ability to find full or partial matches against a query pattern, but also a means to quantify the pace of the appearance of the indicators. This technology has a variety of potential applications such as aiding in the detection of homegrown violent extremists before they carry out acts of domestic terrorism, detecting signs for post-traumatic stress in veterans, or tracking potential customer activities and experiences along a consumer journey. We propose a vectorized graph pattern matching approach that calculates the multi-hop class similarities between nodes in query and data graphs over time. By tracking partial match trajectories, we provide another dimension of analysis in investigative graph searches to highlight entities on a pathway towards a pattern of a latent behavior. We demonstrate the performance of our approach on a real-world BlogCatalog dataset of over 470K nodes and 4 million edges, where 98.56% of nodes and 99.65% of edges were filtered out with preprocessing steps, and successfully detected the trajectory of the top 1,327 nodes towards a query pattern.