项目名称: 具有耦合性结构的多视图社交网络社区发现算法研究及其应用
项目编号: No.61502543
项目类型: 青年科学基金项目
立项/批准年度: 2016
项目学科: 其他
项目作者: 王昌栋
作者单位: 中山大学
项目金额: 21万元
中文摘要: 随着信息技术的发展,产生了越来越多的多视图社交网络数据。多视图社交网络社区发现不仅仅是社交网络数据分析的基础,同时也是一个亟待解决的科学难题。多视图社交网络社区发现算法研究的关键在于如何有效地整合多视图社交行为信息,挖掘出的社区结构不仅仅需要刻画视图内的用户社交行为,同时还需要反映不同视图之间用户社交行为的跨视图耦合性。本项目拟以模块度理论、谱分析方法、二分法、矩阵向量化技巧、跨视图邻域结构分析、向心度、因子图模型、最大乘积置信传播等作为理论和技术基础,重点研究基于模块度的弱耦合多视图社交网络社区发现模型的优化求解和基于向心度的强耦合多视图社交网络社区建模及其优化求解问题,建立具有耦合性结构的多视图社交网络社区发现新算法及其在多视图社交网络数据分析中的应用。本项目的开展将进一步丰富数据挖掘理论和方法,特别是推动多视图社交网络社区发现研究的发展。
中文关键词: 多视图;社交网络;社区发现;耦合性
英文摘要: With the development of information technology, we are generating more and more multi-view social network data. Community detection in multi-view social network is not only a fundamental problem of social network data analysis but also a challenging research issue. The key to community detection in multi-view social network is to properly combine information of social behaviors from multiple views so as to discover communities that not only characterize the within-view social behaviors, but also reflect the coupling structure of social behaviors across different views. In this project, based on the modularity theory, spectral analysis, bisection, matrix vectorization trick, cross-view neighbor structure analysis, centrality, factor graph model, and max-product belief propagation, we aim to address the following problems associated with community detection in multi-view social network with coupling structure, namely, the optimization problem of modularity-based model for community detection in multi-view social network with weak coupling, and modeling of centrality-based community detection in multi-view social network with strong coupling, as well as its optimization problem. And then we will apply the proposed methods to the tasks of multi-view social network data analysis. The project would further enrich the theories and methods of data mining. In particular, it would enhance the research development of community detection in multi-view social network.
英文关键词: Multi-view;Social network;Community detection;Coupling