利用信任和社会网络的资源发现方法

为解决无结构对等网络中因恶意节点的欺诈行为造成的搜索不可靠问题,提出了利用信任和
社会网络(TSN)的资源发现方法;提出转发信任度、路由信任度和交易信任度的概念,用来
反映节点提供可靠服务的能力. 在节点选择朋友进行消息转发时,优先选择综合信任度高且
相关度高的朋友节点转发查询消息. 实验结果表明,TSN方法在资源搜索阶段有效屏蔽了恶
意节点,保证了请求节点获得服务资源的可靠性,提高了搜索效率.     

PAIDD: a hybrid P2P-based architecture for improving data distribution in social networks

Rapid growth in social networks(SNs)presents a unique scalability challenge for SN operators because of the massive amounts of data distribution among large number of concurrent online users.A request from any user may trigger hundreds of server activities to generate a customized page and which has already become a huge burden.Based on the theoretical model and analytical study considering realistic network scenarios,this article proposes a hybrid P2P-based architecture called PAIDD.PAIDD fulfills effective data distribution primarily through P2P connectivity and social graph among users but with the help of central servers.To increase system efficiency,PAIDD performs optimized content prefetching based on social interactions among users.PAIDD chooses interaction as the criteria because user’s interaction graph is measured to be much smaller than the social graph.Our experiments confirm that PAIDD ensures satisfactory user experience without incurring extensive overhead on clients’network.More importantly,PAIDD can effectively achieve one order of magnitude of load reduction at central servers.     

A social interaction system based on cloud computing for mobile video telephony

As the use of smartphones become popular,people heavily depend on smartphone applications to deal with their social activities.For this reason,traditional message texting between mobile applications does not fulfill the versatile requirements of social networking.Many mobile applications use multimodality to deliver multimedia messages including sticker,voice and photo message,video call,and snap movie to enhance the communicative capability.However,without face-to-face interaction,people may fail to detect the other side’s non-verbal social behavior such as fine-grain facial expressions,body movements,or hand gesture.During social interaction,non-verbal behavior conveys information about the involved individuals and help the speakers express their social emotion in an implicit way.It is so important for real-world face-to-face interaction but is often blocked on the mobile telephony.To cope with this problem,we propose an afective computing model to assist the representation of social emotion and then help the progress of social interaction on the mobile telephony.In this model,for the purpose of real-time afective analysis,we delegate the computing loading to the cloud side service and enhance the system’s scalability and availability.The result of this experiment approves the feasibility of our system design for the applications of social intelligent.Also,the system provides a research framework of the social intelligent system on the mobile telephony.     

Temporal evolution of contacts and communities in networks of face-to-face human interactions

Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes, important influence factors, their effects, and their structural implications. In this article, we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific coil- ferences. On a structural level, we focus on static and dynamic properties of the contact graphs. Also, we analyze the resulting community structure using state-of-the-art automatic community detection algorithms. Specifically, we analyze tile evolution of contacts and communities over time to consider the stability of the respective conmmnities. Furthermore, we assess different factors which have an influence on the quality of com- munity prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.     

基于图卷积网络的微博新闻故事线抽取方法

微博作为人们获取和传播新闻事件的主要平台,隐藏着丰富的事件信息。从微博数据中抽取故事线能为用户提供一种直观的方式来准确理解事件演化,然而微博数据稀疏和上下文缺乏的特点为故事线抽取带来了挑战。因此,通过两个连续的任务从微博数据中自动抽取故事线：1）基于微博传播影响力对事件进行建模,并提取出首要事件;2）基于事件特征建立异构事件图,提出事件图卷积网络（E-GCN）模型来提升对事件间隐式关系的学习能力,从而实现事件的故事分支预测并链接事件。在真实数据集上从故事分支和故事线两个角度进行评测,结果表明所提方法在故事分支生成测评中,相较于贝叶斯模型、斯坦纳树和故事森林在F1值上,在Dataset1上分别高出28个百分点、20个百分点和27个百分点,在Dataset2上分别高出19个百分点、12个百分点和22个百分点;而在故事线抽取评测中,相较于故事时间线、斯坦纳树和故事森林在正确的边准确率上,在Dataset1上分别高出33个百分点、23个百分点和17个百分点,在Dataset2上分别高出12个百分点、3个百分点和9个百分点。     

强关系型社交媒体平台不实信息传播模型研究

社交媒体是当前人们获取信息的重要渠道,但是社交媒体中不实信息大量传播造成社会危害的问题一直困扰着管理者。为了减轻不实信息造成的不良社会影响,对不实信息的传播规律进行研究。划分出强关系型社交媒体,通过Multi-Agent建模的方法,引入用户个体异质性和决策行为不确定性,建立不实信息传播模型。用控制变量法,在多种网络中进行仿真模拟。研究表明：不实信息传播的初始节点不完全能够决定不实信息的传播范围;不实信息传播过程的缓解期长短与网络的随机性有关;用户观点共鸣度分布会对不实信息传播产生影响。     

青山嘴水库移民城市楼房安置后续发展趋势研究

云南青山嘴水库工程因地制宜,在国内首次提出将移民安置于城市楼房并享受20 a生活补助的安置方式,但20 a生活补助期满后移民的后续发展是否还能适应城市的社会经济发展尚不可知。以托达罗理论为基础,建立了移民在城市楼房安置后可持续发展的理想模型,并用时间序列法在现有数据的基础上对移民未来的经济能力进行预测,探寻基于现有政策的移民后续经济能力发展的趋势;结合我国十九大提出两步走战略安排,计算出预测结果与发展目标间的差距,并以此作为后续发展过程中移民所需帮扶的依据,提出帮助移民经济能力达到发展目标的具体措施。     

基于MRIO与SNA的中国水资源空间转移网络分析

基于2002年、2007年和2012年的多区域投入产出表的相关数据,利用多区域投入产出(MRIO)模型和社会网络分析法(SNA)分析了中国水资源流动网络的空间关联性和结构特征。结果表明:网络中的链接数量没有随水足迹贸易值的增加而增加,链接路径上的流量不断增加,网络的韧性不够,空间关联渠道有待拓展,存在一定的等级差异,稳定性需要加强;网络中一些省份链接的增加,会削弱其他省份的链接数目,这种可塑性特征有利于在全国范围内合理配置水资源;将网络划分为4个板块,板块间的溢出关系主要是以板块内部为主,且少数板块之间没有任何的溢出关系。     

基于动态偏好机制的学生群体演化合作研究

社交网络具有随机性和复杂性的特点,针对大学生群体中社交关系的异质性以及动态变化情况,构建博弈模型并设计动态偏好机制用于模拟和促进学生在交互网络中的合作行为。在该机制中,首先构建了加权网络用于描述学生交互网络的异质性;然后通过学生动态的交互关系,模拟学生的社交偏好。仿真结果表明,该机制能够有效促进合作者密度。为了进一步验证该机制在学生群体交互中的有效性,通过线下社交网络收集系统收集学生群体的交互行为信息并构建交互网络,通过数据分析发现学生群体在动态偏好机制下更愿意与其他个体进行交流。研究结果对学生合作行为演化具有参考作用。     

高职高专新生社交焦虑现状调查分析

以某高职高专院校400名新同学为研究对象,采用交往焦虑量表(IAS)为调查研究工具,对高职院校新生社交焦虑现状进行调查。调查结果用SPSS 16.0统计软件进行分析,结果显示,目前的高职高专新生比一般本科院校学生,在人际交往的主观感受方面,社交焦虑程度偏高,且随性别、是否担任学生干部、所学科目类别、生源地、家庭经济因素等条件的不同会产生差异。通过对调查结果的仔细分析,提出从事心理辅导的教师、辅导员及班主任重视并关注社交焦虑得分较高、有疑似社交焦虑恐惧症状的同学,采用合适的方法给予心理辅导,使其逐步消除主观社交焦虑情绪,建立和谐良好的人际关系的建议。