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Internet of Things
Published in Neeraj Kumar, Aaisha Makkar, Machine Learning in Cognitive IoT, 2020
In recent years, there has been an exponential increase in the usage of the Internet. It is widely used for information retrieval. This information is gathered, stored, and processed at the central repository known as a web server. The server represents this information in the form of web documents, which are accessible with the help of the Internet. According to the Statista report, a web information provider company, the number of Internet users in 2018 was 369.01 million. The platform used for accessing the Internet is mostly the dedicated designed software known as a web search engine. According to NETMARKETSHARE, a market share statistics provider, the largest market share of search engines is achieved by Google, i.e., 72.03%, followed by Baidu(14.11%), Bing(7.76%) and Yahoo(4.27%), accessed on September 2018. The reason behind the success of web search engines are the search engine result pages (SERPs). These pages are ranked by the ranking methodology which considers the important features of a web page. PageRank is the ranking algorithm used by Google for ranking the web pages for SERPs.
Agent-based modelling for cultural networks
Published in Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg, Handbook of Computational Social Science, Volume 2, 2021
Fernando Sancho-Caparrini, Juan Luis Suárez
Generically, PageRank is the name given to the various versions of a web page ranking algorithm that was designed by Larry Page and Sergey Brin when they were at Stanford University (Page et al., 1999). In addition to the impact on the world of computation that this algorithm has for the problem it solves, it has great historical, technological and social importance because its creation gave rise to the invention of Google’s search engine. The algorithm provides a method to measure the importance of a web page within a system of pages, based on the quantity and quality of links pointing to it.
Advancements and Innovation in Digital Marketing and SEO
Published in Abid Hussain, Garima Tyagi, Sheng-Lung Peng, IoT and AI Technologies for Sustainable Living, 2023
Anubha Jain, Chhavi Jain, Rahul G. Kargal, Salini Suresh
PageRank algorithms rank the websites on the search results; the greater the PageRank, search engines consider it more important. A combination of on-page methods and off-page techniques are employed for the improvement of page ranking. Even though on-page methods do website optimization, off-page methods cannot be undervalued as it is very significant to create traffic and therefore enhance ranking.
The identification of influential nodes based on structure similarity
Published in Connection Science, 2021
Jie Zhao, Yutong Song, Fan Liu, Yong Deng
PageRank algorithm (Brin & Page, 1998; Page et al., 1999) is used to rank website in Google. PageRank identifies the importance of the websites by random walking on the network. PageRank assumes that the importance of a webpage is determined by both the quantity and the quality of the pages linked to it. Initially, each node (i.e. page) has the same PR value. Then every node evenly distributes the PR value to its neighbours and the PR value of node at t step is where n is the number of nodes in the network and is the out-degree of node . The iteration will stop when the PR values of all nodes reach the steady state. s is the coefficient, which refers to the probability of reaching a certain page and continuing to browse backwards at any time.
A multi-layer modelling approach for mining versatile ports of a global maritime transportation network
Published in International Journal of Digital Earth, 2023
Peng Peng, Christophe Claramunt, Shifen Cheng, Yu Yang, Feng Lu
PageRank is the initial ranking strategy originally applied to evaluate web hubs and authorities (Brin and Page 1998). PageRank mainly reveals the role of a single webpage in relation to other web pages. The more a page is linked to by other pages, the more important it is to have a high PageRank value. The more quality pages are linked to, the more important the page with a high PageRank value is. Under similar principles in the maritime network, the PageRank algorithm not only considers the number of ports connected to other ports but also whether these connected ports include some important ports. The formula can be derived as follows:
An Intelligent Whole-Process Medical System Based on Cloud Platform
Published in Applied Artificial Intelligence, 2023
The TextRank algorithm, which is based on the popular PageRank algorithm, is utilized to generate keywords and summaries for text. By utilizing the TextRank algorithm, the system determines the significance of sentences within an article and selects the most important ones in order to provide answers to patients. The original purpose of the PageRank algorithm was to measure the importance of web pages. This algorithm treats a single website as a directed graph, with the links within the webpage serving as the direction between each point. Once the graph is constructed, the PageRank algorithm calculates the importance of the web page through a specific formula: