As a whole, the World Wide Web displays a striking ''rich get richer'' behavior, with a relatively small number of sites receiving a disproportionately large share of hyperlink references and traffic. However, hidden in this skewed global distribution, we discover a qualitatively different and considerably less biased link distribution among subcategories of pages-for example, among all university homepages or all newspaper homepages. Although the connectivity distribution over the entire web is close to a pure power law, we find that the distribution within specific categories is typically unimodal on a log scale, with the location of the mode, and thus the extent of the rich get richer phenomenon, varying across different categories. Similar distributions occur in many other naturally occurring networks, including research paper citations, movie actor collaborations, and United States power grid connections. A simple generative model, incorporating a mixture of preferential and uniform attachment, quantifies the degree to which the rich nodes grow richer, and how new (and poorly connected) nodes can compete. The model accurately accounts for the true connectivity distributions of category-specific web pages, the web as a whole, and other social networks.

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These findings suggest that a mobile, machine learning process is a reliable method for detection of autism outside of clinical settings. A variety of confounding factors in the clinical analysis are discussed along with the solutions engineered into the algorithms. Final results are statistically limited and will benefit from future clinical studies to extend the sample size.

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SynopsisThis paper describes the development, validation and use of a computerized assessment for minor psychiatric disorder based on the Clinical Interview Schedule (CIS; Goldberg et al. 1970). There was good agreement between the computerized assessment and the CIS administered by psychiatrists, both in assessing overall severity and in defining ‘cases’ of psychiatric disorder. Individual symptoms elicited by the computer and the CIS were compared, and the levels of agreement found were similar to those from inter-observer studies of standardized interviews. Subjects from a variety of non-psychiatric settings regarded the assessment as acceptable, accurate and easy to use. It is concluded that this computerized assessment of neurotic symptoms is valid and reliable. It eliminates observer bias, it is an efficient use of research resources and it may have clinical applications in primary care.