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In this paper, we propose a solution on microaggregation problem based on the hierarchical tree equi-partition (HTEP) algorithm. Microaggregation is a family of methods for statistical disclosure control of microdata, that is, for masking microdata, so that they can be released without disclose private information on the underlying individuals. Knowing that the microaggregation problem is non-deterministic polynomial-time-hard, the goal is to partitionNgiven data into groups of at leastKitems, so that the sum of the within-partition squared error is minimized. The proposed method is general and it can be applied to any tree partition problem aiming at the minimization of a total score. The method is divisive, so that the tree with the highest ‘score’ is split into two trees, resulting in a hierarchical forest of trees with almost equal ‘score’ (equipartition). We propose a version of HTEP for microaggregation (HTEPM), that is applied on the minimum spanning tree (MST) of the graph defined by the data. The merit of the HTEPM algorithm is that it solves optimally some instances of the multivariate microaggregation problem on MST search space in. Experimental results and comparisons with existing methods from literature prove the high performance and robustness of HTEPM. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient clustering algorithm that has been applied to the microaggregation problem. The goal is to partition $(N)$ given records into clusters, each of them grouping at least $(K)$ records, so that the sum of the within-partition squared error (SSE) is minimized. We propose a successive Group Selection algorithm that approximately solves the microaggregation problem in $(O(N^2 \log N))$ time, based on sequential Minimization of SSE. Experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

One of the fundamental problems in social networking with a lot of potential applications is to detect effectively the communities that are created by the users' interaction. Other applications, such as finding web communities, uncovering the structure of social networks, or even analyzing a graph's structure to uncover Internet attacks are equally as important. All these problems converge to a common goal, the development of flexible and efficient local community detection algorithms. In this paper, we demonstrate the performance of an algorithm that uncovers the entire community structure of a network, based solely on local interactions between neighboring nodes and a distributed clustering algorithm. The proposed algorithm, named VCD, is based on the distributed computation of a synthetic coordinates for each graph node. Experimental results and comparisons with another method from the literature (Lancichinetti et al.) are presented. The algorithm is also tested on two real dataset graphs from the SNAP: Stanford Large Network Dataset Collection. In all cases the experimental results demonstrate the high performance of our algorithm in terms of accuracy to detect communities, and its computational efficiency. [ABSTRACT FROM AUTHOR]