Influencing the Pagerank Using Link Analysis in SEO
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Search Engine Optimization: a Survey of Current Best Practices
Grand Valley State University ScholarWorks@GVSU Technical Library School of Computing and Information Systems 2013 Search Engine Optimization: A Survey of Current Best Practices Niko Solihin Grand Valley Follow this and additional works at: https://scholarworks.gvsu.edu/cistechlib ScholarWorks Citation Solihin, Niko, "Search Engine Optimization: A Survey of Current Best Practices" (2013). Technical Library. 151. https://scholarworks.gvsu.edu/cistechlib/151 This Project is brought to you for free and open access by the School of Computing and Information Systems at ScholarWorks@GVSU. It has been accepted for inclusion in Technical Library by an authorized administrator of ScholarWorks@GVSU. For more information, please contact [email protected]. Search Engine Optimization: A Survey of Current Best Practices By Niko Solihin A project submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Information Systems at Grand Valley State University April, 2013 _______________________________________________________________________________ Your Professor Date Search Engine Optimization: A Survey of Current Best Practices Niko Solihin Grand Valley State University Grand Rapids, MI, USA [email protected] ABSTRACT 2. Build and maintain an index of sites’ keywords and With the rapid growth of information on the web, search links (indexing) engines have become the starting point of most web-related 3. Present search results based on reputation and rele- tasks. In order to reach more viewers, a website must im- vance to users’ keyword combinations (searching) prove its organic ranking in search engines. This paper intro- duces the concept of search engine optimization (SEO) and The primary goal is to e↵ectively present high-quality, pre- provides an architectural overview of the predominant search cise search results while efficiently handling a potentially engine, Google. -
Brand Values and the Bottom Line 1 1
Brand Values and the Bottom Line 1 1. Why You Should Read This Guide 3 2. Common Obstacles to Avoid 4 3. Website Structure 7 4. Keyword Research 8 5. Meta Information 9 Contents 6. Body Content 11 7. Internal Site Linking 12 8. URL Equity 13 9. The Elements of URL Equity 14 10. Assessing URL Equity 15 11. The Consequences of Redesigning Without a URL Strategy 16 12. Migrating URL Equity 17 13. Summary 18 14. Appendix 1 19 15. Appendix 2 20 16. About Investis Digital 21 Brand Values and the Bottom Line 2 1. Why You Should Read This Guide Best Practices: SEO for Website Redesign & Migration outlines organic search optimization best practices for a website redesign, as well as factors to consider in order to maintain the URL equity during a domain or platform migration. This guide illustrates the common pitfalls that you can avoid in the redesign phase of a website, making it possible for a site to gain better visibility within search engines results. Additionally, Best Practices: SEO for Website Redesign & Migration explains the importance of setting up all aspects of a website, To do a deep dive including: directory structure, file names, page content and internal into SEO for website linking. Finally, we illustrate case study examples of successful site redesign and redesigns. migration, contact Reading this guide will set you up Investis Digital. for SEO success when you undergo a website redesign. The guide will We’re here to help. help you avoid costly errors and gain more traffic, leading to valuable conversions. -
Received Citations As a Main SEO Factor of Google Scholar Results Ranking
RECEIVED CITATIONS AS A MAIN SEO FACTOR OF GOOGLE SCHOLAR RESULTS RANKING Las citas recibidas como principal factor de posicionamiento SEO en la ordenación de resultados de Google Scholar Cristòfol Rovira, Frederic Guerrero-Solé and Lluís Codina Nota: Este artículo se puede leer en español en: http://www.elprofesionaldelainformacion.com/contenidos/2018/may/09_esp.pdf Cristòfol Rovira, associate professor at Pompeu Fabra University (UPF), teaches in the Depart- ments of Journalism and Advertising. He is director of the master’s degree in Digital Documenta- tion (UPF) and the master’s degree in Search Engines (UPF). He has a degree in Educational Scien- ces, as well as in Library and Information Science. He is an engineer in Computer Science and has a master’s degree in Free Software. He is conducting research in web positioning (SEO), usability, search engine marketing and conceptual maps with eyetracking techniques. https://orcid.org/0000-0002-6463-3216 [email protected] Frederic Guerrero-Solé has a bachelor’s in Physics from the University of Barcelona (UB) and a PhD in Public Communication obtained at Universitat Pompeu Fabra (UPF). He has been teaching at the Faculty of Communication at the UPF since 2008, where he is a lecturer in Sociology of Communi- cation. He is a member of the research group Audiovisual Communication Research Unit (Unica). https://orcid.org/0000-0001-8145-8707 [email protected] Lluís Codina is an associate professor in the Department of Communication at the School of Com- munication, Universitat Pompeu Fabra (UPF), Barcelona, Spain, where he has taught information science courses in the areas of Journalism and Media Studies for more than 25 years. -
Analysis of the Youtube Channel Recommendation Network
CS 224W Project Milestone Analysis of the YouTube Channel Recommendation Network Ian Torres [itorres] Jacob Conrad Trinidad [j3nidad] December 8th, 2015 I. Introduction With over a billion users, YouTube is one of the largest online communities on the world wide web. For a user to upload a video on YouTube, they can create a channel. These channels serve as the home page for that account, displaying the account's name, description, and public videos that have been up- loaded to YouTube. In addition to this content, channels can recommend other channels. This can be done in two ways: the user can choose to feature a channel or YouTube can recommend a channel whose content is similar to the current channel. YouTube features both of these types of recommendations in separate sidebars on the user's channel. We are interested analyzing in the structure of this potential network. We have crawled the YouTube site and obtained a dataset totaling 228575 distinct user channels, 400249 user recommendations, and 400249 YouTube recommendations. In this paper, we present a systematic and in-depth analysis on the structure of this network. With this data, we have created detailed visualizations, analyzed different centrality measures on the network, compared their community structures, and performed motif analysis. II. Literature Review As YouTube has been rising in popularity since its creation in 2005, there has been research on the topic of YouTube and discovering the structure behind its network. Thus, there exists much research analyzing YouTube as a social network. Cheng looks at videos as nodes and recommendations to other videos as links [1]. -
Package 'Rahrefs'
Package ‘RAhrefs’ July 28, 2019 Type Package Title 'Ahrefs' API R Interface Version 0.1.4 Description Enables downloading detailed reports from <https://ahrefs.com> about backlinks from pointing to website, provides authentication with an API key as well as ordering, grouping and filtering functionalities. License MIT + file LICENCE URL https://ahrefs.com/ BugReports https://github.com/Leszek-Sieminski/RAhrefs/issues Depends R (>= 3.4.0) Imports assertthat, httr, jsonlite, testthat NeedsCompilation no Encoding UTF-8 LazyData true RoxygenNote 6.1.1 Author Leszek Sieminski´ [aut, cre], Performance Media Polska sp. z o.o. [cph, fnd] Maintainer Leszek Sieminski´ <[email protected]> Repository CRAN Date/Publication 2019-07-28 08:40:02 UTC R topics documented: ahrefs_metrics . 2 ahrefs_reports . 2 rah_ahrefs_rank . 3 rah_anchors . 5 rah_anchors_refdomains . 8 rah_auth . 11 rah_backlinks . 11 1 2 ahrefs_metrics rah_backlinks_new_lost . 14 rah_backlinks_new_lost_counters . 17 rah_backlinks_one_per_domain . 20 rah_broken_backlinks . 23 rah_broken_links . 26 rah_condition . 29 rah_condition_set . 31 rah_domain_rating . 32 rah_downloader . 34 rah_linked_anchors . 36 rah_linked_domains . 39 rah_linked_domains_by_type . 42 rah_metrics . 45 rah_metrics_extended . 47 rah_pages . 50 rah_pages_extended . 52 rah_pages_info . 55 rah_refdomains . 58 rah_refdomains_by_type . 61 rah_refdomains_new_lost . 64 rah_refdomains_new_lost_counters . 66 rah_refips . 68 rah_subscription_info . 71 ahrefs_metrics Ahrefs metrics Description Description of Ahrefs -
Webpage Ranking Algorithms Second Exam Report
Webpage Ranking Algorithms Second Exam Report Grace Zhao Department of Computer Science Graduate Center, CUNY Exam Committee Professor Xiaowen Zhang, Mentor, College of Staten Island Professor Ted Brown, Queens College Professor Xiangdong Li, New York City College of Technology Initial version: March 8, 2015 Revision: May 1, 2015 1 Abstract The traditional link analysis algorithms exploit the context in- formation inherent in the hyperlink structure of the Web, with the premise being that a link from page A to page B denotes an endorse- ment of the quality of B. The exemplary PageRank algorithm weighs backlinks with a random surfer model; Kleinberg's HITS algorithm promotes the use of hubs and authorities over a base set; Lempel and Moran traverse this structure through their bipartite stochastic algo- rithm; Li examines the structure from head to tail, counting ballots over hypertext. Semantic Web and its inspired technologies bring new core factors into the ranking equation. While making continuous effort to improve the importance and relevancy of search results, Semantic ranking algorithms strive to improve the quality of search results: (1) The meaning of the search query; and (2) The relevancy of the result in relation to user's intention. The survey focuses on an overview of eight selected search ranking algorithms. 2 Contents 1 Introduction 4 2 Background 5 2.1 Core Concepts . 5 2.1.1 Search Engine . 5 2.1.2 Hyperlink Structure . 5 2.1.3 Search Query . 7 2.1.4 Web Graph . 7 2.1.5 Base Set of Webpages . 9 2.1.6 Semantic Web . -
Human Computation Luis Von Ahn
Human Computation Luis von Ahn CMU-CS-05-193 December 7, 2005 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Manuel Blum, Chair Takeo Kanade Michael Reiter Josh Benaloh, Microsoft Research Jitendra Malik, University of California, Berkeley Copyright © 2005 by Luis von Ahn This work was partially supported by the National Science Foundation (NSF) grants CCR-0122581 and CCR-0085982 (The Aladdin Center), by a Microsoft Research Fellowship, and by generous gifts from Google, Inc. The views and conclusions contained in this document are those of the author and should not be interpreted as representing official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: CAPTCHA, the ESP Game, Peekaboom, Verbosity, Phetch, human computation, automated Turing tests, games with a purpose. 2 Abstract Tasks like image recognition are trivial for humans, but continue to challenge even the most sophisticated computer programs. This thesis introduces a paradigm for utilizing human processing power to solve problems that computers cannot yet solve. Traditional approaches to solving such problems focus on improving software. I advocate a novel approach: constructively channel human brainpower using computer games. For example, the ESP Game, introduced in this thesis, is an enjoyable online game — many people play over 40 hours a week — and when people play, they help label images on the Web with descriptive keywords. These keywords can be used to significantly improve the accuracy of image search. People play the game not because they want to help, but because they enjoy it. I introduce three other examples of “games with a purpose”: Peekaboom, which helps determine the location of objects in images, Phetch, which collects paragraph descriptions of arbitrary images to help accessibility of the Web, and Verbosity, which collects “common-sense” knowledge. -
Pagerank Best Practices Neus Ferré Aug08
Search Engine Optimisation. PageRank best Practices Graduand: Supervisor: Neus Ferré Viñes Florian Heinemann [email protected] [email protected] UPC Barcelona RWTH Aachen RWTH Aachen Business Sciences for Engineers Business Sciences for Engineers and Natural Scientists and Natural Scientists July 2008 Abstract Since the explosion of the Internet age the need of search online information has grown as well at the light velocity. As a consequent, new marketing disciplines arise in the digital world. This thesis describes, in the search engine marketing framework, how the ranking in the search engine results page (SERP) can be influenced. Wikipedia describes search engine marketing or SEM as a form of Internet marketing that seeks to promote websites by increasing their visibility in search engine result pages (SERPs). Therefore, the importance of being searchable and visible to the users reveal needs of improvement for the website designers. Different factors are used to produce search rankings. One of them is PageRank. The present thesis focuses on how PageRank of Google makes use of the linking structure of the Web in order to maximise relevance of the results in a web search. PageRank used to be the jigsaw of webmasters because of the secrecy it used to have. The formula that lies behind PageRank enabled the founders of Google to convert a PhD into one of the most successful companies ever. The uniqueness of PageRank in contrast to other Web Search Engines consist in providing the user with the greatest relevance of the results for a specific query, thus providing the most satisfactory user experience. -
100+ Free SEO Tools & Resources
100+ Free SEO Tools & Resources Keyword Tools ● Keywords Everywhere Great keyword tool Chrome and Firefox extension. (now paid) ● Answer The Public Aggregated view of the questions asked to Google & Bing. ● Keyword Keg Another free keyword tool, you can import lists and export 20 results. ● Wordtracker Scout Displays what articles are about on keyword cloud. ● LSI Graph Generate a list of semantically related keywords and best performing content. ● Google Trends Compare one keyword to another over time ● Keyword Finder Uses Google's autocomplete API to find many keywords. ● KeywordTool.io An alternative to Google keyword planner. ● Merge Words Takes words from 3 lists and merges them together into one. ● Cognitive SEO Keywords Analyze keywords and get suggestions. ● Seed Keywords Grow your own seo keywords from suggestions from your friends. ● Keyword Density Checker Remember to write for people, not SEO, don’t overoptimize. Keyword Rankings ● Small SEO Tools Rank Checker Check rankings of up to 10 keywords at a time. ● Hoth Rankings Tracker Check 10 keywords ranking daily. ● Can I Rank Explains the opportunities and benefits of ranking for certain keywords Backlink Tools ● Ahrefs Backlink Checker Free version of Ahrefs' backlink checker tool. ● Open Link Profiler Fast backlinks and a lot more info worth investigating. ● Check My Links Chrome plugin to check pages for broken links. ● Search Queries for backlink prospecting ● Guide to getting backlinks Lots of great ideas for obtaining a good link or two. ● Help A Reporter Be a reference for reporters looking for answers. Image Optimization ● GeoImgr Image geotagging, add GPS info to photos online. ● EXIF Editor A program for download that allows you to edit EXIF data of images. -
Backlink Building Guide Version 1.0 / December 2019
Backlink Building Guide Version 1.0 / December 2019 Backlink Building Guide Why Backlinks are important Backlinks (links) is and has historically been one of the most important signals for Google to determine if a website or a page content is of importance, valuable, credible, and useful. If Google deems a specific piece of content to have higher importance, be more valuable, more credible and more useful, it will rank higher in their Search Engine Result Page (SERP). One of the things Google uses to determine the positions in SERP is something called page rank, and it’s a direct correlation between the number of backlinks and page rank. Therefore, the more backlinks a website has, the higher the likelihood that the site will also rank higher on Google. • Backlinks are one of the most importance factors of ranking in SERP • Backlinks result in higher probability of ranking higher in SERP Backlink Building Guide Version 1.0 / December 2019 The different types of Backlinks Currently there are two different types of backlinks, follow (also called dofollow) and nofollow. A follow backlink means that Google's algorithms follow the links and the page rank on the receiving website increases. A nofollow backlink is still a link and users can still follow it from one page to another, but Google does not consider nofollow backlinks in its algorithms i.e. it does not produce any effect to rank higher in SERP. The owner of the website decides if it’s a “follow” or “no follow” backlink. Common nofollow backlinks are links that website owners have no control over and are produced by others, for example, comment on blogs, forum posts or sponsored content. -
The Pagerank Algorithm and Application on Searching of Academic Papers
The PageRank algorithm and application on searching of academic papers Ping Yeh Google, Inc. 2009/12/9 Department of Physics, NTU Disclaimer (legal) The content of this talk is the speaker's personal opinion and is not the opinion or policy of his employer. Disclaimer (content) You will not hear physics. You will not see differential equations. You will: ● get a review of PageRank, the algorithm used in Google's web search. It has been applied to evaluate journal status and influence of nodes in a graph by researchers, ● see some linear algebra and Markov chains associated with it, and ● see some results of applying it to journal status. Outline Introduction Google and Google search PageRank algorithm for ranking web pages Using MapReduce to calculate PageRank for billions of pages Impact factor of journals and PageRank Conclusion Google The name: homophone to the word “Googol” which means 10100. The company: ● founded by Larry Page and Sergey Brin in 1998, ● ~20,000 employees as of 2009, ● spread in 68 offices around the world (23 in N. America, 3 in Latin America, 14 in Asia Pacific, 23 in Europe, 5 in Middle East and Africa). The mission: “to organize the world's information and make it universally accessible and useful.” Google Services Sky YouTube iGoogle web search talk book search Chrome calendar scholar translate blogger.com Android product news search maps picasaweb video groups Gmail desktop reader Earth Photo by mr.hero on panoramio (http://www.panoramio.com/photo/1127015) 6 Google Search http://www.google.com/ or http://www.google.com.tw/ The abundance problem Quote Langville and Meyer's nice book “Google's PageRank and beyond: the science of search engine rankings”: The men in Jorge Luis Borges’ 1941 short story, “The Library of Babel”, which describes an imaginary, infinite library. -
The Pagerank Algorithm Is One Way of Ranking the Nodes in a Graph by Importance
The PageRank 13 Algorithm Lab Objective: Many real-world systemsthe internet, transportation grids, social media, and so oncan be represented as graphs (networks). The PageRank algorithm is one way of ranking the nodes in a graph by importance. Though it is a relatively simple algorithm, the idea gave birth to the Google search engine in 1998 and has shaped much of the information age since then. In this lab we implement the PageRank algorithm with a few dierent approaches, then use it to rank the nodes of a few dierent networks. The PageRank Model The internet is a collection of webpages, each of which may have a hyperlink to any other page. One possible model for a set of n webpages is a directed graph, where each node represents a page and node j points to node i if page j links to page i. The corresponding adjacency matrix A satises Aij = 1 if node j links to node i and Aij = 0 otherwise. b c abcd a 2 0 0 0 0 3 b 6 1 0 1 0 7 A = 6 7 c 4 1 0 0 1 5 d 1 0 1 0 a d Figure 13.1: A directed unweighted graph with four nodes, together with its adjacency matrix. Note that the column for node b is all zeros, indicating that b is a sinka node that doesn't point to any other node. If n users start on random pages in the network and click on a link every 5 minutes, which page in the network will have the most views after an hour? Which will have the fewest? The goal of the PageRank algorithm is to solve this problem in general, therefore determining how important each webpage is.