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社交网络中的重要节点对于信息的传播效率有着至关重要的作用,也是近年来的研究热点问题.同时,随着新媒体时代手机、微博、微信等新兴媒介日益变快的信息传播速度,政府部门和企业已经逐渐认识到通过识别社交网络中的重要节点对于管理和控制社交网络中的信息传播,在面向应急的非常规突发事件数据获取与分析中,有着举足轻重的作用.新媒体时代也扩展了人们社会活动的信息容量与交换速度,以MapReduce为代表的分布式计算系统在应急管理的大规模社交网络数据分析中也变得越来越普遍.为了便于应急管理中的信息传播控制,针对应急管理中大规模社交网络图上重要节点识别的关键问题,本文提出了一种新颖的基于轴节点选择策略的大图重要节点中介度近似计算方法和原型系统,并通过模拟数据和真实数据(包含一个连续六个月的真实社交网络数据集)进行了验证.实验结果表明,该方法能非常有效地找出社交网络上的重要节点,对于应急管理中的信息传播控制有着重要的作用.
Important nodes in social networks play an important role in the efficiency of information dissemination and are also the hot issues in recent years.While with the increasing speed of information dissemination of emerging media such as mobile phones, Weibo and WeChat in the new media era, Government departments and enterprises have come to realize that playing a crucial role in emergency data acquisition and analysis for emergencies through identifying important nodes in social networks to manage and control the information dissemination in social networks plays a decisive role in the new media era But also expands the information capacity and exchange speed of people’s social activities.The distributed computing system represented by MapReduce also becomes more and more common in large scale social network data analysis of emergency management.In order to facilitate the control of information dissemination in emergency management , Aiming at the key problems of identifying important nodes on large-scale social network in emergency management, this paper presents a novel method and prototype system for approximate calculation of large-scale important nodes based on the selection of pivot nodes. Through the simulation data and the real Data (contains a real social network data set for six months in a row) Line verification. The experimental results show that this method can be very effective to identify important node on the social network for information dissemination emergency management control plays an important role.