Social network analysis with data fusion

This paper reports on the utility of social network analysis methods in the data fusion domain. Given fused data that combine 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 parallel computing-based methodology to extract a social network of individuals from fused data, captured as a cumulative associated data graph (CDG). To obtain the desired social network, two approaches including a hop count weighted and a path salience approach are developed and compared. A supervised learning framework is implemented for parameterizing the extraction algorithms. Parameters utilized in the extraction algorithm consider paths between individuals within the social network, weighing relationships between these individuals based on the count weighted and the path salience calculation methodologies. An overall link strength value is then calculated by aggregating path hop count weights and saliences between unique individual pairs for the hop count weighted and path salience approaches, respectively. Ordered centrality-based HVI lists are obtained from the CDGs constructed from the Sunni criminal thread and Bath'est resurgence threads of the SYNCOIN data set, 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. The computational results demonstrate superiority of path salience approach in identifying HVIs. The insights generated by these approaches and directions for future research are discussed.