基于引力场优化的扩展边重叠社区发现及应用研究

项目名称： 基于引力场优化的扩展边重叠社区发现及应用研究

项目编号： No.61472159

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 计算机科学学科

项目作者： 黄岚

作者单位： 吉林大学

项目金额： 83万元

中文摘要： 社区发现试图从海量数据和复杂网络中提取具有实际意义的共性群体信息，为数据挖掘、算法设计、工程实现和实际应用等诸多领域带来新的挑战和机遇。重叠社区发现由于更贴近实际网络情况，逐渐成为复杂网络分析领域中的前沿课题；而边重叠社区发现将基于传统点关系的重叠社区拓展为基于边关联关系的群体，成为重叠社区发现中新的热门研究方向之一。 本项目拟在边重叠社区框架基础上，扩展边重叠社区发现算法，力争在算法理论方面有新的突破；在扩展的算法基础上，引入引力场优化算法，减小搜索空间并提高计算效率，同时进行相关优化模型和优化算法的理论研究；开展整体算法的并行化实现研究，完善其实用性；针对汽车营销决策、超市客户群分和购买取向、医疗信息、蛋白质网络结构等不同应用领域中的复杂数据进行数据处理与重叠社区建模；新研发的重叠社区分析理论算法与软件实现，对医疗卫生、商品营销、社区服务、政府监控等领域均具有很好的现实意义和应用价值。

中文关键词： 重叠社区；启发式算法；复杂网络；引力场优化

英文摘要： Community detection, which means trying to find meaningful common group information from big data and complex networks. It brings us the new challenges and opportunities in data mining, algorithm design, project realization, real application and some other fields. Since overlapping community detection is more suitable for the analysis in real networks than traditional community detection, it has became the forefront of the complex network analysis field. At the same time, link clutering community detection study the groups of related links instead of traditional view of overlapping community detection which is based on nodes method. This has become a new hot topic in the research field of overlapping community detection. The project firstly aims to develop extended link clustering community detection algorithms, which will be designed under the framework of link clustering community detection and try to make some breakthroughs in the theory of these algorithms; And then, we will introduce a gravitation field algorithm into this extended algorithms to narrow down the searching space and increase its computatinal efficiency. Simutaneously, we decide to imply our research on the related optimization models and the certain theory of algorithms. After that, our research direction will change to the parallelization of the whole algorithms and improve its practicality. By meantime, we will take data processing and modelling work in various fields which have complex data, such as automobile marketing decisions, supermarket customer clustering and behavior analysis, medical information analysis, protein-protein interaction network analysis and so on. We believe that the theory and software relization of our new overlapping community detection algorithm will make good practical significance and have the value of applications to public health, product marketing, community service, government surveillance and other fields.

英文关键词： Overlapping Community;Heuristics Algorithm;Complex Network;Gravitation Field Optimization