Link analysis in various forms is now an established technique in many different subjects, reflecting the perceived importance of links and that of the web. A critical but very difficult issue is how to interpret the results of social science link analyses. It is argued that the dynamic nature of the web, its lack of quality control and the online proliferation of copying and imitation mean that methodologies operating within a highly positivist, quantitative framework are ineffective. Conversely, the sheer variety of the web makes qualitative methodologies and pure reason very problematic to apply to large-scale studies. Methodology triangulation is consequently advocated, in combination with a warning that the web is incapable of giving definitive answers to large-scale link analysis research questions concerning social factors underlying link creation. Finally, it is claimed that whilst theoretical frameworks with which to guide research are appropriate, a Theory of Link Analysis is not possible.

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Webometric network analyses have been used to map the connectivity of groups of web sites in order to identify clusters, important sites or overall structure. Such analyses have mainly been based upon hyperlink counts, the number of hyperlinks between a pair of web sites, although some have used title mentions or URL citations instead. The ability to automatically gather hyperlink counts from Yahoo! ceased in April 2011 and the ability to manually gather such counts was due to cease by early 2012, creating a need for alternatives. This article assesses URL citations and title mentions as possible replacements for hyperlinks in both binary and weighted direct link and co-inlink network diagrams. It also assesses three different types of data for the network connections: hit count estimates, counts of matching URLs and filtered counts of matching URLs. Results from analyses of US library and information science departments and UK universities give evidence that metrics based upon URLs or titles can be appropriate replacements for metrics based upon hyperlinks for both binary and weighted networks, although filtered counts of matching URLs are necessary to give the best results for co-title mention and co-URL citation network diagrams.