基于特征提取与分层建模的社交网络信息传播预测研究

项目名称： 基于特征提取与分层建模的社交网络信息传播预测研究

项目编号： No.61503312

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 陈雁

作者单位： 西南石油大学

项目金额： 20万元

中文摘要： 传播问题是复杂网络理论的重要研究课题之一，其中关于信息传播预测的研究成果，在舆情控制、广告宣传等众多社会媒体应用领域越来越显示出其指导意义。本项目以在线社交网络的信息传播为研究对象，结合复杂网络分析方法和统计学习方法，旨在分析潜在的传播规律，推断节点产生传播行为的可能性，并进一步通过传播动力学模型预测传播的规模和结果。具体的研究内容包括：建立基于节点特征的社交网络分层模型，并分析信息在不同特征层及层间的传播规律；提出基于特定内容的节点特征重要性度量方法，继而建立计算节点传播概率的算法；结合分析和计算的结果，设计信息传播的动力学模型，从而对社交网络上的传播行为进行模拟，最终实现对整个传播过程和结果的预测。本项目以复杂网络的前沿理论为基础，社交网络大规模数据为支撑，结合信息采集技术和机器学习算法，展开多学科交叉研究，研究成果将为准确预测信息传播，指导实际的相关问题提供重要参考。

中文关键词： 网络传播；特征；多层网络；预测

英文摘要： The problem of propagation is an important research topic in the area of complex networks. Predicting the propagation of information has significant guiding value to opinion controlling, advertising and a series of application fields of social media. Our work is based on the method of complex networks and statistical learning theory. The object of our study is to find out the potential pattern of information spreading, and to deduce the possibility of spreading the information by a node, and then to predict the procedure and result of a propagation process through the dynamical model that would be made..The details are as follows: Firstly, multilayer model will be built based on a feature of the node to analyze the rule of how information flow within and across the layers；Then to find a way measuring the importance of a node for the spreading of a particular information and figure out the probability of a node to spread the message; Finally, a model of propagation dynamics will be designed to simulate the process of spreading, which can help to forecast the possible results of the propagation..Our research is based on the cutting-edge theory of complex networks, with the support of the large scale data of social networks. It is also a joint work of information collection and machine learning. So the results of this work would provide an important reference to the solution of lots of practical application fields.

英文关键词： network propagation;feature;multilayer networks;predict