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 a...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.展开更多
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 co...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.展开更多
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 popula...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.展开更多
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 n...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.展开更多
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...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.展开更多
文摘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.
基金supported by Jiangsu Social Science Foundation Project(Grant No:20TQC005)Philosophy Social Science Research Project Fund of Jiangsu University(Grant No:2020SJA0500)+2 种基金The National Natural Science Foundation of China(GrantNo.61802155)The Innovation and Entrepreneurship Project Fund for College Students of Jiangsu Police Academy(Grant No.202110329028Y)The“qinglan Project”of Jiangsu Universities.
文摘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.
文摘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.
基金This work was supported in part by the National Natural Science Foundation of China,Grant No.72073041Open Foundation for the University Innovation Platform in the Hunan Province,Grant No.18K103+3 种基金2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property.Hunan Provincial Key Laboratory of Finance&Economics Big Data Science and Technology2020 Hunan Provincial Higher Education Teaching Reform Research Project under Grant HNJG-2020-1130,HNJG-2020-11242020 General Project of Hunan Social Science Fund under Grant 20B16.Scientific Research Project of Education Department of Hunan Province(Grand No.20K021)Social Science Foundation of Hunan Province(Grant No.17YBA049).
文摘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.
基金supported by the National Natural Science Foundation of China(G.Nos.71961025 and 71910107002)Natural Science Foundation of the Inner Mongolia Autonomous Region(G.No.2019MS07020)Young Talents of Science and Technology in the Universities of the Inner Mongolia Autonomous Region(G.No.NJYT-20-B08).
文摘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.
