基于微博媒体的社区发现技术研究

微博社交网络是由节点构成的,每个节点代表一个微博用户。节点与节点间存在着关系,因此连接紧密的节点形成了社区。如何从微博社交网络中挖掘出社区,已成为Web2．0的团体挖掘研究热点。详细介绍了传统的网络团体挖掘算法,并提出了一种新的社区发现的算法,称为基于用户兴趣的社区发现算法。该算法不论在计算效率还是社区发现效果上比传统算法都具有明显的提升,取得了不错的实验效果。     

基于关系的微博重要度算法研究

近年来微博已经发展为一个影响力巨大的社交网络平台。针对微博的复杂网络特性,提出了对微博用户重要度的算法,从海量微博信息中智能提取重要内容。算法考虑到社交网络的节点间影响以及用户行为的传递性,同时为了提高对海量节点的处理能力,进行了有效的子网分划。通过实验,证明算法能有效分析网络中节点贡献内容的重要度,有助于提高社交网络中舆情监控、分析、管理等工作的效率和准确性。     

基于混合权重合并策略的社交网络用户关注点识别方法

主题模型是用于识别博客、网络社区、微博等社交网络平台上用户关注点的重要手段。考虑到社交网络平台上短文本主题识别的特殊性,该文根据短文本内容在上下文上的相关性,提出一种基于混合权重合并策略的AW-LDA模型。该模型将符合上下文相关条件的短文本进行虚拟合并,并根据上下文相关程度对不同短文本赋予不同的权重,构建了一种新的短文本主题识别方法。通过网络BBS社区与微博社区两组数据的实验,该模型能够有效识别不同话题下社交网络用户关注点,为解决短文本主题识别问题提供了新的解决思路。     

一种基于WordNet的短文本语义相似性算法

 短文本语义相似性计算在文献检索、信息抽取、文本挖掘等方面应用日益广泛.本文提出了一种短文本语义相似性计算算法ST-CW.此算法使用WordNet和Brown文集来计算文本中的概念相似性,在此基础上提出了一个新的方法综合考虑概念、句法等信息来计算短文本的语义相似性.在R&;B及Miller数据集上进行实验,实验结果验证了算法的有效性.     

基于树状结构的语义相似度计算方法分析

文中提出了基于树状结构的语义相似度计算方法.结合概念节点之间的语义关系、语义距离、概念节点的深度、密度对语义相似度的影响,利用树的层次关系来表达概念节点之间的语义信息,并对概念节点密度的计算进行改进,加入了可调节的参数,以适应不同的情景.通过实验验证了该算法在查准率方面具有较强的优越性.     

一种分布式中文微博热点话题的发现方法

针对微博数据文本内容短小、特征词稀疏以及规模庞大的特点,提出了一种基于MapReduce编程模型的发现微博热点话题的方法。该方法首先利用隐主题分析技术解决了微博内容短小、特征词稀疏的问题,然后利用CURE算法缓解了Kmeans算法对初始点敏感的问题,最后采用基于MapReduce编程模型Kmeans聚类算法,对海量微博短文本数据进行快速聚类。实验结果表明该方法可以有效提高微博热点话题发现的效率。     

基于多维扩展特征与深度学习的微博短文本情感分析

该文提出了一种基于深度信念网络(DBN)和多维扩展特征的模型,实现对中文微博短文本的情感分类。为降低传统文本分类方法在处理微博短文时特征稀疏的影响,引入社交关系网络作为扩展特征,依据评论者和博主之间的社交关系,提取相关评论扩展原始微博,将扩展后的多维特征作为深度信念网络的输入。通过叠加多层玻尔兹曼机(RBM)构建DBN模型底层网络结构,多层玻尔兹曼机可以对原始输入抽象并获得数据的深层语义特征。在多个RBM层上叠加一层分类玻尔兹曼机(ClassRBM),实现最终情感分类。实验结果表明,通过调整模型参数和网络结构,构建的深度学习模型在情感分类中能够获得比SVM和NB等浅层分类系统更优的结果,另外,实验证明使用扩展多维特征方法可提高短文本情感分类的性能。     

基于社区发现的舆情热点挖掘研究

信息时代的快速发展,让社交网络平台成为人们交流工作、发表意见、联络感情的第二世界。为了准确地在大量网络舆情数据中发现热点并分析其热度,提出了基于社区发现的舆情热点挖掘研究,使用社区发现算法对舆情数据的热点挖掘工作进行研究,鉴于微博数据传播机制的特点,微博话题的热度值会受到转发量、评论数、点赞次数以及发表时间的影响,为此引入微博舆情数据关键词的热度影响因子来提高针对微博舆情数据热度计算的准确率。并在此基础上提出一种基于综合权重计算和热度影响因子的改进算法—CWHIF-TR。构建针对微博文本的热度分析模型,完成热点关键词热度分析实验和摘要句热度分析实验,为热度计算的相关研究提供思路。     

结合词性的短文本相似度算法及其在文本分类中的应用

针对基于语义的短文本相似度计算方法在短文本分类中准确率较低这一问题,提出了结合词性的短文本相似度算法( GCSSA)。该方法在基于hownet(“知网”)语义的短文本相似度计算方法的基础上,结合类别特征词并添加关键词词性分析,对类别特征词和其他关键词的词性信息给定不同关键词以不同的权值系数,以此区别各种贡献度词项在短文本相似度计算中的重要程度。实验表明,该算法进行文本相似度计算后应用于短文本分类中较基于hownet的短文本分类算法在准确率宏平均和微平均上提升4%左右,有效提高了短文本分类的准确性。     

社交网络中用户区域影响力评估算法研究

以人人网为例对在线社交网络的分析,从区域信息传播的角度出发,研究社交网络中,信息传播的微观过程.通过真实测量用户的信息传播行为,完成用户信息传播网络的构建和测量.发现区域信息传播网络中少量核心节点覆盖了大部分的网络传播行为.针对这些核心节点,文中提出了一种基于节点传播意愿和传播能力综合考察的节点传播影响力识别算法InfluenceRank,并通过与多种相关算法进行比对,验证了算法的有效性.     

一种基于移动用户行为的回路融合社区发现算法

针对现有基于派系的重叠社区发现算法难以对移动社会化网络实施的问题,该文给出一种基于移动用户行为的回路融合社区发现算法。该算法首先通过分析移动用户行为构建移动社会化网络,利用k-EC(k-Elementary Circuit)简单回路发现算法寻找移动社会化网络的k阶回路作为社区核,并按照给定的规则对社区核进行融合,得到初步社区;然后通过计算移动用户行为的相关度将余下的离散节点加入到相应的初步社区,得到最终的社区;最后通过公开数据集和仿真数据集验证了该算法在移动社会化网络社区发现方面的可行性和有效性。     

基于种子节点选择的网络环境下多标签分类算法研究

多标签分类在基因分类,药物发现和文本分类等实际问题中有着广泛的应用.已存在的多标签分类算法,通常都是从网络中随机的选取节点作为训练集.然而,在分类算法执行的过程中,网络中不同节点所起的作用不同.在给定训练集数目的情况下,选择的训练集不同,分类精度也会不同.所以我们引入了种子节点的概念,标签分类从种子节点开始,经过不断推理,得到网络中其他所有节点的标签.本文提出了SHDA（Nodes Selection of High Degree from Each Affiliation）算法,即从网络的每个社团中,按比例的选取度数较大的节点,然后将其合并,处理后得到种子节点.真实数据集上的实验表明,将种子节点用作训练集进行多标签分类,能够提升网络环境下多标签分类的准确率.     

Semantic analysis on social networks: A survey

As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.     

Low-latency neighbor discovery algorithm based on multi-beacon message in mobile low-duty-cycle sensor network

Neighbor discovery enables nodes in the networks to discover each other through simple information interaction,which was suitable for the new mobile low duty cycle sensor network (MLDC-WSN).However,because the nodes in MLDC-WSN can move randomly and sleep,the network topology was changed frequently,which results in that some nodes need a lot of energy and time to find their neighbors.How to realize fast neighbor discovery for all nodes in the network was a difficult problem in current research.To solve this problem,a new low-latency neighbor discovery algorithm based on multi-beacon messages was proposed.In this algorithm,the nodes were discovered by sending a short beacon message through their neighbor nodes,and by adjusting the time and frequency of beacon message sent,a lower neighbor discovery delay was obtained.Eventually,through quantitative analysis and simulation experiments,it is found that compared with existing algorithms,this algorithm can find all neighbor nodes in MLDC-WSN with less energy consumption,lower latency and greater probability.     

An asynchronous neighbor discovery algorithm for wireless sensor networks

We consider the problem of determining, in a distributed, asynchronous and scalable manner, what nodes are “neighbors” in a wireless network. Neighbor discovery is an important enabler of network connectivity and energy conservation. An asynchronous, probabilistic neighbor discovery algorithm is presented that permits each node in the network to develop a list of its neighbors, which may be incomplete. The algorithm is analyzed and parameter settings are derived which maximize the fraction of neighbors discovered in a fixed running time. A companion distributed algorithm is also described which allows all the nodes in the network to execute that neighbor discovery algorithm without the need to agree on a common start time.     

Key nodes of the trust transitivity in social Internet of things

The trustee and the trustor may have no previous interaction experiences before. So, intermediate nodes which are trusted by both the trustor and the trustee are selected to transit trust between them. But only a few intermediate nodes are key nodes which can significantly affect the transitivity of trust. To the best of our knowledge, there are no algorithms for finding key nodes of the trust transitivity. To solve this problem, the concept of trust is presented, and a comprehensive model of the transitivity of trust is provided. Then, the key nodes search (KNS) algorithm is proposed to find out the key nodes of the trust transitivity. The KNS algorithm is verified with three real social network datasets and the results show that the algorithm can find out all the key nodes for each node in directed, weighted, and non-fully connected social Internet of things (SIoT) networks.     

Energy saving proactive neighbor discovery algorithm in mobile low-duty-cycle wireless sensor network

Mobile low-duty-cycle wireless sensor network is a new kind of wireless multi-hop network,which is self-organized by a large number of nodes that have mobile ability and are able to get into sleep for a long time.Such networks have wide application prospects in national defense,industry,agriculture and other fields that need long term monitoring in severe environments.However,the movement and the sleeping features of nodes lead to constantly change of network topology,which makes the nodes difficult to discover their neighbors quickly.Therefore,the nodes cannot achieve optimal distribution decisions.In order to solve this problem,a new proactive neighbor discovery algorithm was proposed.This algorithm made the nodes in the network take the initiative to find their neighbors when they woke up,and avoided the delay caused by long time waiting in the traditional passive neighbor discovery.In addition,by predicting the movement speed and distance of neighbors,the neighbor set at the next moment can be quickly determined,which can further reduce the delay and obtain more accurate neighbor discovery results.Theoretical analysis and experimental results show that compared with the existing algorithms,the algorithm can find all the neighbors in MLDC-WSN with less energy consumption and lower delay.     

基于信息损失量估计的匿名图构造方法

首先分析了在进化的社会网络序列中,攻击者利用节点度信息,通过识别目标节点的方法对局部社会网络进行攻击过程,分析了利用k匿名方法对该类攻击进行隐私保护时存在的信息损失问题,针对该问题,提出了一种基于信息损失量估计的k匿名图流构造方法,通过子图节点属性泛化、子图内部结构的泛化控制图重构的信息损失,通过禁止子图内部扰动阻止网络攻击。定义匿名过程中由于图重构造成的节点和结构信息损失的估算方法,建立了基于贪婪聚类算法的网络节点的k匿名聚类算法,根据信息损失估计实现匿名分组,在进化的社会网络中以最小信息损失量构造匿名社会网络,在医疗诊断数据集上的实验表明所提方法能够较理想地控制信息损失量。     

基于社会网络面向个性化需求的可信服务推荐

在当前服务计算和社会计算背景下,针对难以获取满足用户个性化需求的可信Web服务问题,给出基于社会网络面向个性化需求的可信Web服务推荐模型;设计用户个性化功能需求分解与匹配算法,并利用语义词典提高功能需求语义匹配的准确性;基于个性化功能需求、社会网络节点信任度及服务信任度,设计了一种满足用户个性化需求的可信服务推荐算法,通过对社会网络节点之间、节点与服务之间的信任相关性进行分析,提高服务协同可信推荐性能。算法分析及实验结果表明该方法是有效和可行的。