Examining Clandestine Social Networks for the Presence of Non-Random Structure

This thesis develops a tractable, statistically sound hypothesis testing framework for the detection, characterization, and estimation of non-random structure in clandestine social networks. Network structure is studied via an observed adjacency matrix, which is assumed to be subject to sampling variability. The vertex set of the network is partitioned into k mutually exclusive and collectively exhaustive subsets, based on available exogenous nodal attribute information. The proposed hypothesis testing framework is employed to statistically quantify a given partition\u27s relativity in explaining the variability in the observed adjacency matrix relative to what can be explained by chance. As a result, valuable insight into the true structure of the network can be obtained. Those partitions that are found to be statistically significant are then used as a basis for estimating the probability that a relationship tie exists between any two vertices in the complete vertex set of the network. The proposed methodology aids in the reduction of the amount of data required for a given network, focusing analyses on those attributes that are most promising. Ample effort is given to both model demonstration and application, including an example using open-source data, illustrating the potential use for the defense community and others

A tandem clustering process for multimodal datasets

Clustering multimodal datasets can be problematic when a conventional algorithm such as k-means is applied due to its implicit assumption of Gaussian distribution of the dataset. This paper proposes a tandem clustering process for multimodal data sets. The proposed method first divides the multimodal dataset into many small pre-clusters by applying k-means or fuzzy k-means algorithm. These pre-clusters are then clustered again by agglomerative hierarchical clustering method using Kullback-Leibler divergence as an initial measure of dissimilarity. Benchmark results show that the proposed approach is not only effective at extracting the multimodal clusters but also efficient in computational time and relatively robust at the presence of outliers. (c) 2004 Elsevier B.V. All rights reserved.X113sciescopu