基于观测数据的社交网络推断方法研究

Research on Social Network Inference Method Based on Observation Data

互联网技术和在线社交网络迅速发展,使得人们可以随意发表个人意见、思想,进行感情交流和经济往来。通过在互联网上人们交流的观测数据,使得对社交网络的推断成为可能。通过对ConNIe (Convex Network Inference)算法的分析,研究了稀疏参数、传播时间分布模型及其参数对此算法推断结果的影响。依据前面的分析,提出了基于ConNIe算法的社交网络推断框架,结合了感知器算法和粒子群优化算法,使得ConNIe算法推断成为一个完整的体系。在社会舆情管控、市场营销等领域有广泛的实用价值。

Internet technology and online social networks have developed rapidly, which enables people to randomly express their opinions, ideas, emotional exchanges and economic exchanges. Inferring social networks is made possible through the observation data exchanged by people on the Internet. Through the analysis of ConNIe(Convex Network Inference) algorithm, this paper researches the effects of sparse parameter, propagation time distribution model and its parameters on the inference results of the algorithm. According to the analysis, a social network inference framework based on ConNIe algorithm is proposed. Combining the perceptron and particle swarm optimization algorithm, the ConNIe algorithm inference becomes a complete system. The research in this paper has a widely practical value in the fields of social public opinion control and marketing.