In this article, we define webometrics within the framework of informetric studies and bibliometrics, as belonging to library and information science, and as associated with cybermetrics as a generic subfield. We develop a consistent and detailed link typology and terminology and make explicit the distinction among different Web node levels when using the proposed conceptual framework. As a consequence, we propose a novel diagram notation to fully appreciate and investigate link structures between Web nodes in webometric analyses. We warn against taking the analogy between citation analyses and link analyses too far.

Whilst hyperlinks within Web sites may be primarily created for navigation purposes, those between sites are a rich source of information about the content and use of the Web. As a result there is a need to derive descriptive statistics about them, both to help understand the underlying communication processes and so that policy makers can gain insights into the use of online information by those located within their constituency. It is known, however, that using the individual web link source page as the basic unit of counting is problematical because of the number and size of link anomalies. The challenge addressed in this paper is that of developing methods to assess techniques for counting links from groups of large university web sites (site outlinks). Two methods to assess the reliability of link counts are developed and applied to judge which of seven advanced document models are most appropriate in each case. The most generally applicable method used is an internal consistency test based upon a highly simplified model of web linking behaviour. The data used comes from crawls of UK, Australian and New Zealand universities. The standard domain advanced web document model emerges as the logical choice for comparison purposes within this set. Some descriptive statistics concerning Top Level Domain link targets are given and it is demonstrated that the choice of model can effect the final results.

A layered approach for identifying communities in the Web is presented and explored by applying the Flake Exact Community Identification Algorithm to the UK academic Web. Although community or topic identification is a common task in information retrieval, a new perspective is developed by: (a) the application of Alternative Document Models, shifting the focus from individual pages to aggregated collections based upon Web directories, domains and entire sites; (b) the removal of internal site links; and (c) the adaptation of a new fast algorithm to allow fully automated community identification using all possible single starting points. The overall topology of the graphs in the three least aggregated layers was first investigated and found to include a large number of isolated points but, surprisingly, with most of the remainder being in one huge connected component, exact proportions varying by layer. The community identification process then found that the number of communities far exceeded the number of topological components, indicating that community identification is a potentially useful technique, even with random starting points. Both the number and size of communities identified was dependant on the parameter of the algorithm, with very different results being obtained in each case. In conclusion, the UK academic Web is embedded with layers of non-trivial communities and, if it is not unique in this, then there is the promise of (a) improved results for information retrieval algorithms that can exploit this additional structure, and (b) the application of the technique directly to partially automate Web metrics tasks such as that of finding all pages related to a given subject hosted by a single country’s universities.

We introduce several new versions of PageRank (the link based Web page ranking algorithm), based upon 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 upon directories and domains gives promising alternatives, particularly when Web links are the object of study. The new algorithms introduced based upon 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.

This study describes how search engines (SE) can be employed for automated, efficient data gathering for Webometric studies using predictable URLs. It then compares the usage of staffrelated Web Impact Factors (WIFs) to sizerelated impact factors for a ranking of Australian universities, showing that rankings based on staffrelated WIFs correlate much better with an established ranking from the Melbourne Institute than commonly used sizedependent WIFs. In fact sizedependent WIFs do not correlate with the Melbourne ranking at all. It also compares WIF data for Australian Universities provided by Smith (1999) for a longitudinal comparison of the WIF of Australian Universities over the last decade. It shows that sizedependent WIF values declined for most Australian universities over the last ten years, while staffdependent WIFs rose.

This study describes how search engines (SE) can be employed for automated, efficient data gathering for Webometric studies using well defined query specfic URLs in SE (predictable URLs). It then compares the usage of staff-related Web Impact Factors (WIFs) to web impact factors for a ranking of Australian universities, showing that rankings based on staff-related WIFs correlate much better with an established ranking from the Melbourne Institute than commonly used WIFs. In fact WIFs do not correlate with the Melbourne ranking at all. It also compares WIF data for Australian Universities provided by Smith [1] for a longitudinal comparison of the WIF of Australian Universities over the last decade. It shows that size-dependent WIF values declined for most Australian universities over the last ten years, while staffdependent WIFs shows a riding trend. 1.