大规模动态多维社会网络的社区发现算法研究

项目名称： 大规模动态多维社会网络的社区发现算法研究

项目编号： No.61303167

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 赵中英

作者单位： 山东科技大学

项目金额： 23万元

中文摘要： 社区结构研究，对理解网络结构和功能、揭示网络中的隐含模式、分析及预测网络行为等具有非常重要的理论意义。同时，还可以应用在智能推荐、精准营销等诸多领域，具有广泛的应用价值。然而，网络的大规模性、动态性、多维性等特点，对现有的社区发现算法提出了挑战。本课题拟针对大规模、动态、多维社会网络，研究如下内容：(1)探索有效的多维网络建模方法；(2)基于多维网络建模，研究多维社区的评价方法及发现算法；(3)基于增量学习技术，研究增量的动态社区发现算法以及增量的多维社区动态发现算法；(4)结合算法机理和并行性分析，研究适用于大规模、动态、多维社会网络的算法优化技术。最后，基于上述研究结果，设计开发一个面向大规模复杂网络的社区发现软件系统。本课题的研究结果不仅能够为社会网络分析与挖掘领域提供理论基础，而且还能够为广告投放、个性化推荐、精准营销等相关领域提供科学分析的依据，具有重要的理论意义和应用价值。

中文关键词： 社区发现；社交媒体；数据挖掘；多维网络；动态网络

英文摘要： Studying community structures has a very important theoretical significance. It helps us understand the structures and functions of networks, reveal the implicit patterns, analyze and predict the network behaviors. Meanwhile, studying community structures has a wide range of application prospects. It is able to be applied to many fields, such as business intelligence, intelligent recommendation， and social marketing. However, the characteristics of large scale, dynamic and multi-dimensional of social networks, are putting forward the new challenges to the existing methods of community detection. This project aims to study the effective and efficient algorithms to detect communities in large scale, dynamic and multi-dimensional social networks. We first study and effective data modeling methods based on tensor. Based on multi-dimensional model, we explore the evaluating metrics and study the algorithm to detect multi-dimensional communities. Based on incremental machine learning techniques, we explore some incremental algorithms to detect dynamic communities, multi-dimensional dynamic communities. Furthermore, we study the optimizing techniques to improve the complexity and parallelism of the above algorithms. With the above theoretical studies, we will design and develop a software system which is oriented to co

英文关键词： community detection；social media；data mining；multi-dimensional network；dynamic network