Improved Key Node Recognition Method of Social Network Based on PageRank Algorithm

The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture,and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand.There are key node users in social networks.Compared with ordinary users,their influence is greater,their radiation range is wider,and their information transmission capabilities are better.The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites.In order to solve the problems of incomplete evaluation factors,poor recognition rate and low accuracy of key nodes of social networking sites,this paper establishes a social networking site key node recognition algorithm(SNSKNIS)based on PageRank(PR)algorithm,and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social networks.This article takes the Sina Weibo platform as an example,uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network,analyzes its importance in the social network,and displays it visually.

Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

A Bias-Free Time-Aware PageRank Algorithm for Paper Ranking in Dynamic Citation Networks

The process of ranking scientific publications in dynamic citation networks plays a crucial rule in a variety of applications. Despite the availability of a number of ranking algorithms, most of them use common popularity metrics such as the citation count, h-index, and Impact Factor (IF). These adopted metrics cause a problem of bias in favor of older publications that took enough time to collect as many citations as possible. This paper focuses on solving the problem of bias by proposing a new ranking algorithm based on the PageRank (PR) algorithm;it is one of the main page ranking algorithms being widely used. The developed algorithm considers a newly suggested metric called the Citation Average rate of Change (CAC). Time information such as publication date and the citation occurrence’s time are used along with citation data to calculate the new metric. The proposed ranking algorithm was tested on a dataset of scientific papers in the field of medical physics published in the Dimensions database from years 2005 to 2017. The experimental results have shown that the proposed ranking algorithm outperforms the PageRank algorithm in ranking scientific publications where 26 papers instead of only 14 were ranked among the top 100 papers of this dataset. In addition, there were no radical changes or unreasonable jump in the ranking process, i.e., the correlation rate between the results of the proposed ranking method and the original PageRank algorithm was 92% based on the Spearman correlation coefficient.

Detection approach for unusable shared bikes enabled by reinforcement learning and PageRank algorithm

Existing research models can neither indicate the availability of shared bikes nor detect unusable ones owing to a lack of information on bike maintenance and failure.To improve awareness regarding the availability of shared bikes,we propose an innovative approach for detecting unusable shared bikes based on reinforcement learning and the PageRank algorithm.The proposed method identifies unusable shared bikes depending on the local travel data and provides a ranking of the shared bikes according to their availability levels.Given a sliding time window,the value function for the reinforcement learning model was determined by considering the cumulative number of unavailable shared bikes,the proportion of rental cancelations at the same stations,and the mean time between the cancelations.Reinforcement learning was then used to identify shared bikes with the worst availability.An availability ranking for the shared bikes below the reward threshold was performed using the PageRank algorithm.The proposed detection approach was applied to a trip dataset of a real-world bike-sharing system to illustrate the modeling process and its effectiveness.The detection results of unusable shared bikes in the absence of failure and feedback data can provide essential information to support the maintenance management decisions regarding shared bikes.

A PageRank-Based WeChat User Impact Assessment Algorithm

In recent years,the mobile Internet has developed rapidly,and the network social platform has emerged as the times require,and more people make friends,chat and share dynamics through the network social platform.The network social platform is the virtual embodiment of the social network,each user represents a node in the directed graph of the social network.As the most popular online social platform in China,WeChat has developed rapidly in recent years.Large user groups,powerful mobile payment capabilities,and massive amounts of data have brought great influence to it.At present,the research on WeChat network at home and abroad mainly focuses on communication and sociology,but the research from the angle of influence is scarce.Therefore,based on the basic principle of PageRank,this paper proposes an influence evaluation model WURank algorithm suitable for WeChat network users.This algorithm takes into account the shortcomings of the traditional PageRank algorithm,and objectively evaluates the real-time influence of WeChat users from the perspective of WeChat user behavior(including:sharing,commenting,mentioning,collecting,likes)and time factors.

基于改进PageRank算法的银行零售业务客户价值排名研究

在银行零售业务交易系统中,如何在大量客户数据交易网络中挖掘出影响力高,潜在价值高的重要发展客户,从而制定相应的业务营销计划,对银行来说是一件至关重要的事情.本文提出一种基于PageRank的改进算法——IER(Improved Enhanced-RatioRank)算法,该算法以客户作为节点,以主动交易金额构成出链权重因子作为有向边,构成一个客户交易网络有向图,通过添加交易次数活跃因子和时间有效性因子等重要因素,从多维角度可以精准有效地挖掘出重要发展客户.最后,利用RFM(Recency, Frequency, Monetary)模型来验证实验结果.实验结果表明,所提算法在银行零售业务交易系统中挖掘重要发展客户有良好的效果.

基于PageRank算法的机器人专利推荐系统开发

机器人产业的发展和应用前景十分广阔。通过将机器人技术依赖网络进行可视化,帮助设计人员进行技术元素启发式探索,通过PageRank算法构建更加完整的技术框架,并推荐相关的专利,从而帮助设计人员进行产品创新设计。

基于云模型和PageRank算法的社会网络群决策方法

近年来,随着信息、通信和技术的快速发展,决策者之间的关系也由此变得越来越紧密。在此背景下,决策者基于社会网络关系存在交互,社会网络群体决策会产生比传统群体决策更多的信息,信息不完全性、随机性以及决策者有限理性等因素都会导致群体决策结果与实际情况产生偏差。针对社会网络群体决策中决策偏好信息为定性概念的语言信息,且决策者之间存在信任度的群体决策问题,本文提出了一种基于云模型和PageRank算法的社会网络群决策方法并针对现有的云聚类算法拓展出了社会网络云聚类算法。首先,将语言信息转化为云模型;其次,基于社会网络拓扑图利用PageRank算法来计算每个决策者的权重;接着,使用社会网络云聚类算法将决策者分成几个子聚集并求出子聚集的权重;最后把云模型综合成方案云,利用随机模拟技术获取各个方案云的评分,给出方案的排序,并通过对比分析验证了该方法的有效性。

知识图谱视角下我国股票市场风险传染研究

以我国A股上市公司大数据为基础,深入分析上市公司之间的多层网络关联关系,构建上市公司关联知识图谱,进而提出基于个性化PageRank算法的风险随机游走模型,对风险传染过程进行模拟。首先,运用爬虫技术获取上市公司的多维度关联数据,进而通过实体消歧和实体统一处理实现知识的获取和融合,构建A股上市公司的关联知识图谱;其次,运用图论基本原理将关联图谱转化为风险传染图谱,并将个性化PageRank风险随机游走模型引入到风险图谱中,对突发风险事件的传染过程进行高效的可视模拟和预测。本文所构建的知识图谱包含约15万个节点、18万条关系,支持可视化查询、智能化推理和风险传染模拟多重功能,从人工智能视角为金融风险传染这一复杂过程的模拟计算和高效预警提供了新的研究思路和方法,可为金融风险智能监管等研究提供有益参考。

基于AI算法的自然语言信息提取-翻译-校对系统设计

自20世纪90年代起,随着人工智能(AI)的飞速发展及其与深度学习等机器学习方法的广泛融合,自然语言处理(NLP)作为人工智能的核心,也取得了令人瞩目的进步。而随着国际学术交流、世界文化交融愈加频繁,人们搜寻、阅读他国网络信息的现实需求也随之增多。当信息搜寻者在搜寻非母语信息时,不仅会出现语言障碍问题,还会因错综复杂、层次不齐的各色信息而产生诸多不便。为了便于信息搜寻者快速高效地获取有用信息,文中基于人工智能算法(PageRank/TextRank)设计一种信息提取-翻译-校对(ETP)系统。系统通过AI自动搜索阅读页面上的重要信息和文本摘取,生成摘要,并基于机器翻译API模块完成翻译;其次,采用智能校对系统完成校对审核后,将信息呈现给搜寻者,以供其对全部信息高效且准确地进行预筛选,从而节省阅读时间和精力。最后对系统算法所实现的功能进行实验测试,结果达到预期。

PageRank算法的分析及其改进

在分析PageRank算法存在偏重旧网页、主题漂移、网页权值均分、忽视用户浏览兴趣现象的基础上,对其进行改进,考虑网页修改日期、网页文本信息、网站权威度、用户兴趣度等重要因素,重新计算网页PR值。实验结果表明,改进算法可提高搜索引擎对网页排序的准确度,以及用户对检索结果的满意度。

基于改进PageRank算法的跨链公证人机制评价模型

跨链技术是区块链领域研究和关注的热点,公证人机制是一种典型的跨链技术手段。针对公证人机制中存在节点信用监督不足的问题,构建基于改进PageRank算法的公证人节点信用评价模型。收集多种公证人节点相关信息,利用改进的PageRank算法对公证人节点进行信用计算,得到高可信的公证人节点,从而保证区块链系统安全稳定。将用户评价和节点本身信息进行综合分析和相互验证,使得公证人机制更加安全可信。实验结果表明,该评价模型在跨链公证人机制中具有良好的应用和评估效果。

基于MapReduce的并行PageRank算法实现

分布式网络爬虫的广泛应用使得搜索引擎的数据规模呈几何式增长,面对数以TB甚至PB量级的数据,单机模式下的PageRank算法由于CPU、I/O和内存的开销过大导致效率低下。为此,提出一种基于MapReduce框架的并行PageRank算法。在算法的一次迭代过程中,利用Map函数对网页拓扑信息文件进行解析,使用Reduce函数计算网页得分,从而并行化PageRank算法的中间迭代过程。通过计算全局网页得分控制迭代次数,得到较精确的网页排序结果。实验结果表明,该算法在保持原有单机PageRank算法整体网页排序精度的基础上,具有较好的集群性能和较快的执行速度。

基于主题相似度模型的TS-PageRank算法

PageRank算法是著名搜索引擎Google的核心算法,但存在主题漂移的问题,致使搜索结果中存在过多与查询主题无关的网页.在分析PageRank算法及其有关改进算法的基础上,提出了基于虚拟文档的主题相似度模型和基于主题相似度模型的TS-PageRank算法框架.只要选择不同的相似度计算模型,就可以得到不同的TS-PageRank算法,形成一个网页排序算法簇.理论分析和数值仿真实验表明,该算法在不需要额外文本信息,也不增加算法时空复杂度的情况下,就能极大地减少主题漂移现象,从而提高查询效率和质量.

基于中心性和PageRank的网页综合评分方法

为准确、高效地对网页进行评分,提出了一种基于中心性(结点度、居间度和紧密度)和PageRank算法的网页评分方法CentralRank.它采用PageRank算法计算网页分数,借助中心性度量的方法计算页面在Web社会网络中的重要性.为了验证CentralRank的性能优势,设计了一个网页抓取器,可利用该抓取器自动、准确地下载网页信息.该网页抓取器集成了网络信息采集、页面内容分析和页面消重3项技术.基于大量真实数据的实验结果表明:CentralRank在保证网页评分时间性能的前提下,比单纯基于中心性的网页评分算法和PageRank算法更准确、有效,预测准确性分别提高约14.2%和7.5%.

基于加权PageRank算法的关键包识别方法

识别软件中的关键实体对于人们理解软件,控制和降低维护费用具有重要意义.然而现有的工作基本都是针对关键类识别的,针对关键包、方法/属性等的研究甚少;同时现有的工作也未能揭示关键类与软件外部质量属性间的关系.为丰富现有的工作,本文提出了一种基于加权PageRank算法的关键包识别方法.该方法用加权有向软件网络模型抽象包粒度软件系统,提出新度量PR(PackageRank)从结构角度量度节点重要性,并引入加权的PageRank算法计算该度量值.数据实验部分以六个开源Java软件为例,分析了包的PR值与常用复杂网络中心性指标(介数中心性、接近中心性、度数中心性等)间的相关性;使用加权的SIR(Susceptible-Infectious-Recovered)模型分析了PR所识别关键包的传播影响,并与其它相关方法进行比较,验证了本文方法的有效性;最后,以其中两个软件为例,分析了包的PR值与包可理解性间的关系,进一步验证了本文方法的有效性.

改进的PageRank在Web信息搜集中的应用

PageRank是一种用于网页排序的算法,它利用网页间的相互引用关系评价网页的重要性·但由于它对每条出链赋予相同的权值,忽略了网页与主题的相关性,容易造成主题漂移现象·在分析了几种PageRank算法基础上,提出了一种新的基于主题分块的PageRank算法·该算法按照网页结构对网页进行分块,依照各块与主题的相关性大小对块中的链接传递不同的PageRank值,并能根据已访问的链接对块进行相关性反馈·实验表明,所提出的算法能较好地改进搜索结果的精确度·

基于内容过滤PageRank的Top-k学习资源匹配推荐

针对在线教育支持技术中关于文本处理的多义词和同义词问题,提出基于内容过滤PageRank语义相似替换的Top-k学习资源推荐算法。基于内容的向量空间滤波建立学习资源过滤推荐模型,该模型采用资源间匹配方式以取代语义相似性,从而避免多义词或同义词的漏检问题。基于谷歌PageRank算法结合前述资源间匹配模型构建考虑资源间关系连接的权重矩阵,取代传统PageRank算法网页间的超链接方式,进行资源类型划分,得到特征的马尔可夫收敛矩阵,并利用Top-k算法实现推荐结果细化。实验结果表明,在公共学习资源数据集中,所提算法对计算时间的覆盖率是可行的。

采用PageRank和节点聚类系数的标签传播重叠社区发现算法

基于标签传播的社区发现算法可以检测出复杂网络的重叠社区结构,因此提出了一种基于PageRank和节点聚类系数的重叠社区发现算法。该算法使用PageRank算法对节点的影响力进行排序,可以稳定社区发现结果,节点的聚类系数是一个与节点相关的值,使用节点聚类系数修改算法的参数并限制每个节点拥有最多标签的数量值,可以提高社区挖掘的质量。在人工网络和真实世界的网络上测试,实验验证了该算法能够有效地检测出重叠社区,并具有可接受的时间效率和算法复杂度。

基于改进PageRank算法的输电网关键节点辨识方法研究

为了准确全面辨识输电网的关键输电节点,针对现有辨识方法未考虑节点间功率传输路径的分配关系以及不同类型节点传输特性的问题,基于复杂网络理论和PageRank算法,从静态分析角度建立输电网的有向加权网络模型。根据节点在功率传输路径中的不同特点将节点划分为电源节点、中间节点和终端节点,并考虑不同类型节点与相邻节点间传输转移的信息值及节点间非等概率传输特性,通过对PageRank算法改进得到考虑节点状态信息与拓扑信息的电气传输转移矩阵。在此基础上,结合节点电压值、节点度和节点传输转移特性形成能够准确全面辨识输电网关键节点的方法。通过仿真结果对比分析,并结合系统遭受静态蓄意攻击后网络传输效率变化,表明所辨识关键节点故障后对电网的传输能力影响较大,验证了所提辨识方法的正确性与优越性。