Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.

A Parallel Approach for Sentiment Analysis on Social Networks Using Spark

The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result,social media has emerged as the most effective and largest open source for obtaining public opinion.Single node computational methods are inefficient for sentiment analysis on such large datasets.Supercomputers or parallel or distributed proces-sing are two options for dealing with such large amounts of data.Most parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ficult to use and scale in environments where supercomputers are expensive.Using the Apache Spark Parallel Model,this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk access.Millions of tweets have been classified using the trained model.Experiments with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data sets.It is nearly 12 times quicker than the Map Reduce-based model and nearly 21 times faster than the Naive Bayes Classifier in Apache Mahout.To evaluate the framework’s scalabil-ity,we gathered a large training corpus from Twitter.The accuracy of the classi-fier trained with this new dataset was more than 80%.

Research Hotspots and Trends Analysis of Real-World Data Based on Social Network Analysis and Knowledge Graph

Objective To study the research status,research hotspots and development trends in the field of real-world data(RWD)through social network analysis and knowledge graph analysis.Methods RWD of the past 10 years were retrieved,and literature metrological analysis was made by using UCINET and CiteSpace from CNKI.Results and Conclusion The frequency and centrality of related keywords such as real-world study,hospital information system(HIS),drug combination,data mining and TCM are high.The clusters labeled as clinical medication and RWD contain more keywords.In recent 4 years,there are more articles involving the keywords of data specification,data authenticity,data security and information security.Among them,compound Kushen injection,HIS database and RWD are the top three keywords.It is a long-term research hotspot for Chinese and western medicine to use HIS to study clinical medication,clinical characteristics,diseases and injections.Besides,the research of RWD database has changed from construction to standardized collection and governance,which can make RWD effective.Data authenticity,data security and information security will become the new hotspots in the research of RWD.

How Do the Evolution and Innovation of Social Network Analysis Matter to Computer Science and Communications?

This paper provides a comprehensive overview of evolution and innovation in social network analysis to the paradigm of social networking. It explains how the development of sociological theory and the structural properties of social groups matter to computer science and communications. Authors such as Moreno, John Barnes and Harrison C. White provide evidence of a growing body of literature addressing the networking of people, organizations and communities to explain the structure of society. This perspective has passed from sociology to other fields, changing understandings of social phenomena. Social networks remain a potent concept for analyzing computer science and communications. This paper shows how and why this has occurred and examines substantive areas in which social network analysis has been applied—mainly how the advantages of graphic visualization and computer software packages have influenced SNA in different audiences and publics leading to the unfolding of social networking to different audiences and publics.

Sentiment Analysis on the Social Networks Using Stream Algorithms

The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.

Analysis of Urban Agglomeration Network Structure Based on Baidu Migration Data: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Urban Agglomeration

The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure of urban agglomeration in the Greater Bay Area through the use of social network analysis method.This is the inaugural application of big data based on location services in the study of urban agglomeration network structure,which represents a novel research perspective on this topic.The study reveals that the density of network linkages in the Greater Bay Area urban agglomeration has reached 100%,indicating a mature network-like spatial structure.This structure has given rise to three distinct communities:Shenzhen-Dongguan-Huizhou,Guangzhou-Foshan-Zhaoqing,and Zhuhai-Zhongshan-Jiangmen.Additionally,cities within the Greater Bay Area urban agglomeration play different roles,suggesting that varying development strategies may be necessary to achieve staggered development.The study demonstrates that large datasets represented by LBS can offer novel insights and methodologies for the examination of urban agglomeration network structures,contingent on the appropriate mining and processing of the data.

Modeling Reading and Replying Activities in a BBS Social Network

This paper is devoted to analyze and model user reading and replying activities in a bulletin board system （BBS） social network. By analyzing the data set from a famous Chinese BBS social network, we show how some user activities distribute, and reveal several important features that might characterize user dynamics. We propose a method to model user activities in the BBS social network. The model could reproduce power-law and non-power-law distributions of user activities at the same time. Our results show that user reading and replying activities could be simulated through simple agent-based models. Specifically, manners of how the BBS server interacts with Internet users in the Web 2.0 application, how users organize their reading lists, and how user behavioral trait distributes are the important factors in the formation of activity patterns.

Collective Background Extraction for Station Market Area by Using Location Based Social Network

Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users＇ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS （global positioning system） datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users＇ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users＇ scores and city＇s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.

铁路网络联系对城市土地绿色利用效率的影响研究——以长三角地区为例

研究目的:探究铁路网络联系对城市土地绿色利用效率的影响及空间溢出效应,为依托铁路交通发展促进土地绿色利用效率提升提供科学依据。研究方法:超效率SBM模型,社会网络分析方法,空间面板模型。研究结果:(1)长三角城市铁路联系强度和土地绿色利用效率均呈现显著的增长趋势,土地绿色利用效率存在着较为明显的区域差异。(2)铁路联系强度的提高促进了本城市土地绿色利用效率的提升,与综合铁路联系相比,高铁联系对本城市土地绿色利用效率的提升作用更加明显。(3)铁路联系的加强促进了本城市产业结构合理化水平的提升和创新产出的增长,进而对土地绿色利用效率产生影响。高铁联系在促进本城市产业结构合理化水平提升和创新产出增长方面的作用更加明显。(4)城市对外铁路联系强度的提高产生了负向的空间溢出效应,抑制了邻近城市土地绿色利用效率的提升。研究结论:应充分发挥铁路建设在优化产业结构、促进创新方面的作用,依托铁路网络加强区域内经济技术合作、发挥各城市比较优势,以推动区域土地绿色利用效率的整体性提升。

卫生健康标准中关系型数据共现矩阵计算及SAS程序实现

目的设计卫生健康标准中关系型数据共现矩阵的求解路径及其SAS实现方法。方法文章以计算一组标准的起草单位共现矩阵为例,将“标准-起草单位”二维表格导入SAS(版本号:9.4),按照“两两相乘”的计算思路自行设计宏程序计算起草单位之间的共现矩阵。结果以计算一组标准的起草单位共现矩阵为例,采用自行构建的模拟数据进行演示。首先,导入宏循环起始的数据集,并计算共现频次C_(j,k);然后,导出为out数据集,进一步合并数据集,形成最终的共现矩阵。结论SAS宏程序计算标准中关系型数据共现矩阵具有灵活高效的优势,可用于社会网络分析和互动演进规律总结。

在线社交网络中用户地理位置预测综述

随着智能移动终端的日益普及,人们越来越多地利用社交网络平台(如Twitter、新浪微博等)获取信息、评论和交流.虽然全球卫星定位系统(GPS)设备能够精确获取位置信息,但是大量用户出于隐私和安全的考虑不会直接共享自己的位置信息.因此,如何获取在线用户的地理位置成为了一个前沿的研究领域以及学术界和工业界共同关注的重要课题,并且成为众多下游应用的基础,例如基于位置的定向广告投放、事件/地点的推荐、自然灾害或疾病预警和网络犯罪的追踪等.详细总结了预测社交网络用户地理位置的方法、数据、评价体系和基础算法.首先,归纳了不同的定位任务以及相应的评价指标;其次,针对不同的任务梳理所用的数据类型和数据融合方式,并且,详尽分析了已有的信息抽取和特征选择方式及其优缺点;再次,对现有定位模型和算法进行分类,从地名词典、传统机器学习和深度学习3个方面对用户定位方法进行阐述和分析;最后,总结了社交网络用户地理位置预测的难点和面临的挑战,并展望该领域的发展趋势和未来研究所需要关注的方向.

基于企业数据的京津冀城市群网络结构解析——区域性社会网络和超网络分析的应用

在区域协同合作、经济内循环的背景下,从企业数据出发对京津冀城市群真实的经济关联进行解析。基于社会网络分析方法刻画单层网络结构,并将超网络方法引入分析“地区-行业”网络的层内和层间交互、合作和影响情况。结果表明:(1)基于企业组织测度的地区子网络区域内发展差距大、资源配置失衡,北京都市圈和石家庄都市圈地位突出,天津的辐射能力局限于市域范围内;(2)基于资本流通测度的行业子网络中,制造业、租赁和商务服务业、批发和零售业、金融业、科学研究和技术服务业影响突出;(3)超网络中的重要行业门类相似,北京、天津和石家庄各区县的相关指标均较高,但行业流出的资本更多地去往北京、天津而非石家庄。这些发现对于促进区域创新驱动协同发展具有重要价值。

我国科学数据开放共享作者合著研究

基于社会网络分析方法构建科学数据开放共享领域作者合著网络,并对合著网络的密度、中心性、结构洞和成分指标进行测度分析;在研究核心作者团体的基础上,对该团体的合著网络和研究主题进行分析,揭示出核心作者团体合著网络特点,并挖掘领域内研究主题,以期推动科学数据开放共享研究领域的发展。

基于普查数据的中国省际人口迁移网络演化分析

中国省际人口迁移问题一直以来被广泛关注,研究其特点及变化规律对我国经济社会发展具有重要意义。为此,本文基于第五次、第六次和第七次这三次全国人口普查数据,利用社会网络分析方法,构建了中国省际人口迁移无权有向网络,并计算网络基本拓扑性质、进行节点重要性和凝聚子群分析。研究发现,中国各省之间人口迁移关系越来越紧密,网络的“小世界”现象有上升趋势;东部地区人口吸引力持续保持,但近几年北京市等特大城市人口迁入有所放缓,广东省对于人口迁移一直有较强的吸纳作用,东北地区人口外流现象愈发严重。中部地区和西部地区近几年人口回流现象较为明显,我国各地区经济发展水平趋于均衡化,2015年后各子群间迁移关系明显增多。

基于景观大数据的游客时空行为研究进展

信息和计算机科学技术的快速发展改变了以往游客时空行为相关理论的研究方式。游客在旅游过程中使用社交APP、定位装置和在线媒体会产生大量的景观时空大数据,为游客时空行为研究提供了更多发展空间。文中系统地介绍景观大数据的获取来源和方法,针对景观大数据处理分析方法进行概括,主要是以ArcGIS核密度、社会网络、图片内容分析法3种方法为主。从时间和空间角度,对国内外游客时空行为研究进展进行梳理,总结基于景观大数据的游客时空行为研究热点,未来的研究趋势可能为多空间类型、多采集平台、多时空要素同时开展。

铁路信号机房外部冲击电流监测原理及软件分析策略

针对铁路信号机房应用场景下常见的外部冲击电流问题,从基本监测原理、软件分析策略两方面提出具体解决方案,基于该方案可实现对多频段冲击电流的综合采集以及冲击电流详情自动化分析,实现对设备管理单位的指导性建议,提升铁路信号机房运行的安全性。

基于大数据分析技术探究互联网时代下打造老年网络社交创作平台的可行性及意义

在信息化、数字化与老龄化重叠交汇的时代,老年人成为深受互联网影响的人群之一。互联网时代不仅促进了老年人新型社会交往关系的链接,也为老年人展示自我、结交新朋友创造了条件和机会。从老年人个体来看,在原有家庭、朋友网络之外建立起的第四社交圈对于缓解老年人的孤独、防止老年人的社会隔离和边缘化具有重要作用。文章利用大数据分析技术,旨在从海量数据中挖掘出有价值的信息,并为老年网络社交创作平台的建设提供有力的支持。

图卷积神经网络在社交媒体情感分析的应用研究

文章深入研究图卷积神经网络在社交媒体情感分析中的应用。首先,对图卷积神经网络的基本原理进行简要探讨。其次,详细研究图卷积神经网络在社交媒体情感分析中的应用。最后,采用WASSA-2017数据集进行实证研究,结果表明图卷积神经网络在社交媒体情感分析任务中表现出较高的分类准确性和可信度。

A Diachronic Perspective on the Use of French Spatial Data Infrastructures

Despite the recent development of many worldwide initiatives, there is still a need for the development of observation frameworks that will provide a comprehensive view of SDI’s use. Amongst the many challenges left, a thorough analysis of the information flows between existing SDIs as well as their respective uses and the way that those evolve over time is an important issue to explore. The research presented in this paper introduces a methodological framework oriented to the study of the SDIs use from a diachronic perspective. The approach is based on a Social Network Analysis (SNA) and questionnaires collected by online surveys. We develop a structural and diachronic analysis based on a series of graph-based measures identifying the main patterns that appear over time. The methodological framework is applied to a series of French SDIs and users involved in environmental management. The study identifies a series of structural differences in the data flows that emerge between the users and SDIs. Last, the diachronic network analysis provides an overall understanding on how data flows evolve over time at different institutional levels.