Social network extraction and high value individual (HVI) identification within fused intelligence data

Abstract

This paper reports on the utility of social network analysis methods in the data fusion domain. Given fused data that combines multiple intelligence reports from the same environment, social network extraction and High Value Individual (HVI) identification are of interest. The research on the feasibility of such activities may help not only in methodological developments in network science, but also, in testing and evaluation of fusion quality. This paper offers a methodology to extract a social network of individuals from fused data, captured as a Cumulative Associated Data Graph (CDG), with a supervised learning approach used for parameterizing the extraction algorithm. Ordered, centralitybased HVI lists are obtained from the CDGs constructed from the Sunni Criminal Thread and Bath’est Resurgence Threads of the SYNCOIN dataset, under various fusion system settings. The reported results shed light on the sensitivity of betweenness, closeness and degree centrality metrics to fused graph inputs and the role of HVI identification as a test-and-evaluation tool for fusion process optimization.