and called for "the development of objective, measurable and verifiable criteria of classification based not on per-sonal or parochial considerations, but- on behavioral and other objectively measurable manifestations." Attempts by other investigators to subject clinical observations and judgments

an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks. This paper presents a large-scale measurement study and analysis of the structure

between power and centrality and Cook et al.'s recent exceptional results. I propose a family of centrality measures c(a, 3) generated by two parameters, a and P. The parameter P reflects the degree to which an individual's status is a function of the statuses of those to whom he or she

to these hosts, how often hosts connect and disconnect from the system, how many les hosts share and download, the degree of cooperation between the hosts, and several correlations between these characteristics. Our measurements show that there is significant heterogeneity and lack of cooperation across peers

of this paper are an understanding of their rel ative importance, of how they interact, and of the impl ications for MAC and routing protocol design.

Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a

It is very common to see reported in applied econometric literature time series regression equations with an apparently high degree of fit, as measured by the coefficient of multiple correlation R2 or the corrected coefficient R2, but with an extremely low value for the Durbin-Watson statistic. We

are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the ~entra~~t~ ~ of ~os~tio~ls in a network, and one relenting the degree of centralization of the entire network. The implications of these measures for the experimental study

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key

A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations