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The processing and mining information in large scale graph data have proven to be challenging. The bulk synchronous parallel (BSP) computing model is suitable for this task. In this paper, we implement the multi-level step-wise partitioning (MSP) algorithm in BSP programming model, and replace the original graph partition method. The results on both experimental data and real world data proved this...
Community detection in complex networks has attracted a lot of attentions in recent years. Compared with the traditional single-objective community detection approaches, the multi-objective approaches based on evolutionary computation can provide a decision maker with more flexible and promising solutions. How to make effective use of the optimal solution set returned by the multi-objective community...
Community detection, as an important unsupervised learning problem in social network analysis, has attracted great interests in various research areas. Many objective functions for community detection that can capture the intuition of communities have been introduced from different research fields. Based on the classical single objective optimization framework, this paper compares a variety of these...
Currently, community detection has led to a huge interest in data analysis on real-world networks. However, the high computationally demanding of most community detection algorithms limits their applications. In this paper, we propose a heuristic algorithm to extract the community structure in large networks based on local community attractive force optimization whose time complexity is near linear...
Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). Correspondingly, a special Multi-Objective...
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