We study the relation between PageRank and other parameters of information networks such as in-degree, out-degree, and the fraction of dangling nodes. We model this relation through a stochastic equation inspired by the original definition of PageRank. Further, we use the theory of regular

molecules. The distributions of rigid segments and ring systems, the distributions of molecular paths and circular substructures, and the sizes of molecular similarity clusters all show linear trends on log-log rank/frequency plots, suggesting underlying power-law distributions. The number of unique

, and the use of logic and modifiers, and (iii) terms- their rank/frequency distribution and the most highly used search terms. We then shift the focus of analysis from the query to the user to gain insight to the characteristics of the Web user. With these characteristics as a basis, we then conducted a

exponentially increasing bin widths as pioneered by Preston (1948), and plots of ranked species abundances. While both techniques have been extensively used in the investigation of community ecology hypotheses, the Preston-style species-abundance histogram has become central to current debates concerning

the logs can be exploited to improve the ranking of the results returned by an entity search engine. Entity search engines are becoming more and more popular as the web is changing from a web of documents into a “web of things”. We show that entity search engines pose new challenges since their model

conceptually simple and computationally convenient and may be readily extended to testing nonnonnal distributional hypotheses. The probability plot correlation coefficient test consists of two widely used tools in water resource engineering: the probability plot and the product moment correlation

When the highest-way association is present in a 3-way cross-classification of frequencies, standard logit and loglinear models have an many parameters as there are cells in the table; that is, the models are "saturated. " Extensions of logit and loglinear models are described here

process uses a dataset of system logs from many computer systems to score messages. For better scoring, unsupervised clustering is used to identify sets of systems that behave similarly. We propose a new feature construction scheme that measures the difference in the ranking of messages by frequency

frequency. Results of this analysis suggest that only some estimator values show a correlation with query length or position in the TEL logs (e.g. similarity score between collection and query). Second, the relation between three attributes was investigated: the user’s country (detected from IP address

related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it exploits only limited query log information and performs relatively better on queries in all