The content and design of web sites: an empirical study
Information and Management
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Global corporate web sites: an empirical investigation of content and design
Information and Management
Exploratory Social Network Analysis with Pajek
Exploratory Social Network Analysis with Pajek
Toward a basic framework for webometrics
Journal of the American Society for Information Science and Technology - Special issue: Webometrics
Comparative analysis of webometric measurements in thematic environments: Research Articles
Journal of the American Society for Information Science and Technology
Characterization of national Web domains
ACM Transactions on Internet Technology (TOIT)
Visualization of the Nordic academic web: Link analysis using social network tools
Information Processing and Management: an International Journal
Journal of Information Science
Mapping world-class universities on the web
Information Processing and Management: an International Journal
Expert Systems with Applications: An International Journal
Analysis of virtual communities supporting OSS projects using social network analysis
Information and Software Technology
Strategic group identification using evolutionary computation
Expert Systems with Applications: An International Journal
Modeling the web as a hypergraph to compute page reputation
Information Systems
Expert Systems with Applications: An International Journal
Quantitative evaluation of commercial web sites
International Journal of Information Management: The Journal for Information Professionals
Hi-index | 12.05 |
This paper explores website link structure considering websites as interconnected graphs and analyzing their features as a social network. Two networks have been extracted for representing websites: a domain network containing subdomains or external domains linked through the website and a page network containing webpages browsed from the root domain. Factor analysis provides the statistical methodology to adequately extract the main website profiles in terms of their internal structure. However, due to the large number of indicators, the task of selecting a representative subset of indicators becomes unaffordable. A genetic search of an optimum subset of indicators is proposed in this paper, selecting a multi-objective fitness function based on factor analysis results. The optimum solution provides a coherent and relevant categorization of website profiles, and highlights the possibilities of genetic algorithms as a tool for discovering new knowledge in the field of web mining.