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This study aims to examine the performance of Indian research Institutions’ websites using webometrics by investigating their visibility, traffic ranks, number of links, time on site, Indian/Foreign users and page ranks besides focusing on the two designed hypotheses.

The Council of Scientific and Industrial Research (CSIR) directory (www.csir.res.in/external/heads/aboutcsir/lab_directory.htm) is used to identify the research institutions in India. The directory lists 40 research institutions across India. However, Alexa did not offer required information for some of the websites due to very high traffic ranks, and accordingly the list is reduced to 21 research institutions. The data collected were analysed and tabulated to reveal findings in accordance with the desired objectives.

The results reveal that global traffic ranks, number of page views, number of links and time on site of Indian research institutions are low. However, the page ranks are to some extent satisfactory. The traffic ranks of Indian research institutions differ significantly, whereas no major difference in the page ranks is found. Further, the results show that Indian research institutions’ websites have not been able to attract foreign visitors given the calibre and reputation of these institutions.

The results of this study will be useful for website administrators of research institutions in India and across the globe.

Introduces several new versions of PageRank (the link based Web page ranking algorithm), based on an information science perspective on the concept of the Web document. Although the Web page is the typical indivisible unit of information in search engine results and most Web information retrieval algorithms, other research has suggested that aggregating pages based on directories and domains gives promising alternatives, particularly when Web links are the object of study. The new algorithms introduced based on these alternatives were used to rank four sets of Web pages. The ranking results were compared with human subjects’ rankings. The results of the tests were somewhat inconclusive: the new approach worked well for the set that includes pages from different Web sites; however, it does not work well in ranking pages that are from the same site. It seems that the new algorithms may be effective for some tasks but not for others, especially when only low numbers of links are involved or the pages to be ranked are from the same site or directory.

The purpose of this paper is to identify the most popular techniques used to rank a web page highly in Google.

The paper presents the results of a study into 50 highly optimized web pages that were created as part of a Search Engine Optimization competition. The study focuses on the most popular techniques that were used to rank highest in this competition, and includes an analysis on the use of PageRank, number of pages, number of in‐links, domain age and the use of third party sites such as directories and social bookmarking sites. A separate study was made into 50 non‐optimized web pages for comparison.

The paper provides insight into the techniques that successful Search Engine Optimizers use to ensure a page ranks highly in Google. Recognizes the importance of PageRank and links as well as directories and social bookmarking sites.

Only the top 50 web sites for a specific query were analyzed. Analysing more web sites and comparing with similar studies in different competition would provide more concrete results.

The paper offers a revealing insight into the techniques used by industry experts to rank highly in Google, and the success or otherwise of those techniques.

This paper fulfils an identified need for web sites and e‐commerce sites keen to attract a wider web audience.

This paper aims to focus on the design of algorithms and techniques for an effective set expansion. A tool that finds and extracts candidate sets of tuples from the World Wide Web was designed and implemented. For instance, when a given user provides <Indonesia, Jakarta, Indonesian Rupiah>, <China, Beijing, Yuan Renminbi>, <Canada, Ottawa, Canadian Dollar> as seeds, our system returns tuples composed of countries with their corresponding capital cities and currency names constructed from content extracted from Web pages retrieved.

The seeds are used to query a search engine and to retrieve relevant Web pages. The seeds are also used to infer wrappers from the retrieved pages. The wrappers, in turn, are used to extract candidates. The Web pages, wrappers, seeds and candidates, as well as their relationships, are vertices and edges of a heterogeneous graph. Several options for ranking candidates from PageRank to truth finding algorithms were evaluated and compared. Remarkably, all vertices are ranked, thus providing an integrated approach to not only answer direct set expansion questions but also find the most relevant pages to expand a given set of seeds.

The experimental results show that leveraging the truth finding algorithm can indeed improve the level of confidence in the extracted candidates and the sources.

Current approaches on set expansion mostly support sets of atomic data expansion. This idea can be extended to the sets of tuples and extract relation instances from the Web given a handful set of tuple seeds. A truth finding algorithm is also incorporated into the approach and it is shown that it can improve the confidence level in the ranking of both candidates and sources in set of tuples expansion.

To explore the impact of using metadata in finding and ranking web pages through search engines.

The study has been divided into two phases. In phase one, the use of metadata schemes and the impact of overlapped documents have been examined by employing the usability technique. Phase two examined the impact of adding metadata elements to web pages in their original rank order, using the experimental method. This study focuses on indexing web pages using metadata and its impact on search engine's rankings.

Meta tags are more widely used than Dublin Core. The overlapped pages tend to include metadata. The second phase shows that by adding metadata elements to web pages, it raises its rank order. However, this depends on the quality of the description and the metadata schemes. The study shows no great difference in page ranking between adding meta tags and Dublin Core.

To maximize the impact of metadata, more attention should be given to keyword and descriptive fields.

The hypothetical relationship between overlapped pages and the inclusion of metadata and indexing by search engines had not been previously examined.

To investigate how search engine users manipulate the rankings of search results. Search engines employ different ranking methods in order to display the “best” results first. One of the ranking methods is PageRank, where the number of links pointing to the page influences its rank. The “anchor text,” the clickable text of the hypertext link is another “ingredient” in the ranking method. There are a number of cases where the public challenged the Google's ranking, by creating a so‐called “Google bomb” – creating links to pages they wanted to be highly ranked for given query. Google is chosen as the search engine, because it is currently by far the most popular search engine.

PageRank, one of the major parameters of Google's ranking algorithm is described, and the author explains how this algorithm is exploited by communities of users to promote a certain web page for a specific query. This process is called “Google bombing.” Recent reaction of Google to this phenomenon is also described.

Specific examples of “accomplished Google bombs” show that the public is able to manipulate search results.

Google, instead of being an unobtrusive information retrieval tool has become highly influential in the web scenery. Some users pay for search engine optimization, while others utilize the power of the crowd to influence Google's rankings. This paper supports the claims of Introna and Nissenbaum regarding the power of search engines.

The purpose of this paper is to estimate Google search engine’s ranking function from a search engine optimization (SEO) perspective.

The paper proposed an estimation function that defines the query match score of a search result as the weighted sum of scores from a limited set of factors. The search results for a query are re-ranked according to the query match scores. The effectiveness was measured by comparing the new ranks with the original ranks of search results.

The proposed method achieved the best SEO effectiveness when using the top 20 search results for a query. The empirical results reveal that PageRank (PR) is the dominant factor in Google ranking function. The title follows as the second most important, and the snippet and the URL have roughly equal importance with variations among queries.

This study considered a limited set of ranking factors. The empirical results reveal that SEO effectiveness can be assessed by a simple estimation of ranking function even when the ranks of the new and original result sets are quite dissimilar.

The findings indicate that web marketers should pay particular attention to a webpage’s PR, and then place the keyword in URL, the page title, and snippet.

There have been ongoing concerns about how to formulate a simple strategy that can help a website get ranked higher in search engines. This study provides web marketers much needed empirical evidence about a simple way to foresee the ranking success of an SEO effort.

Literature on organizational analysis identified that project participants have a certain impact on the project outcome. However, there is no study that identifies the impact of project teams and individual project participants on a green building project. The purpose of this paper is to analyze the impact of green building project teams on green building certification.

Project information, project team information, and green building certification grade were collected using the Canadian green building database. Project team data were analyzed and organizations were ranked based on their green building project experience and collaborations with experienced green building organizations. The page rank algorithm is used to calculate the rank of organizations in order to identify the impact of organizational rank on the final green building certification grade of a project.

The results show a positive relationship between the green building certification grade and the number of organizations with more green building experience in a project team. The results also show that not having experienced key organizations such as owners, designers, and contractors will likely lead to a lower green building certification grade.

Impact of project teams on green building projects has not been studied before. This study used an innovative method to analyze green building project teams and to investigate the importance of green building project experience. The findings of this study provided evidence to support the influence of project team compositions in green building projects. The results presented in this paper can help project owners and managers during project team formation for successful execution of green building projects.

This study aims to present a new web page recommendation system that can help users to reduce navigational time on the internet.

The proposed design is based on the primacy effect of browsing behavior, that users prefer top ranking items in search results. This approach is intuitive and requires no training data at all.

A user study showed that users are more satisfied with the proposed search methods than with general search engines using hot keywords. Moreover, two performance measures confirmed that the proposed search methods out‐perform other metasearch and search engines.

The research has limitations and future work is planned along several directions. First, the search methods implemented are primarily based on the keyword match between the contents of web pages and the user query items. Using the semantic web to recommend concepts and items relevant to the user query might be very helpful in finding the exact contents that users want, particularly when the users do not have enough knowledge about the domains in which they are searching. Second, offering a mechanism that groups search results to improve the way search results are segmented and displayed also assists users to locate the contents they need. Finally, more user feedback is needed to fine‐tune the search parameters including α and β to improve the performance.

The proposed model can be used to improve the search performance of any search engine.

First, compared with the democratic voting procedure used by metasearch engines, search engine vector voting (SVV) enables a specific combination of search parameters, denoted as α and β, to be applied to a voted search engine, so that users can either narrow or expand their search results to meet their search preferences. Second, unlike page quality analysis, the hyperlink prediction (HLP) determines qualified pages by simply measuring their user behavior function (UBF) values, and thus takes less computing power. Finally, the advantages of HLP over statistical analysis are that it does not need training data, and it can target both multi‐site and site‐specific analysis.

The spread of subject gateway sites can have an impact on the other major Web information retrieval tool: the commercial search engine. This is because gateway sites perturb the link structure of the Web, something used to rank matches in search engine results pages. The success of Google means that its PageRank algorithm for ranking the importance of Web pages is an object of particular interest, and it is one of the few published ranking algorithms. Although highly mathematical, PageRank admits a simple underlying explanation that allows an analysis of its impact on Web spaces. It is shown that under certain stated assumptions gateway sites can actually decrease the PageRank of their targets. Suggestions are made for gateway site designers and other Web authors to minimise this.