The Anatomy of a Large-Scale Hypertextual Web Search Engine.

TL;DR: In this article, the authors used a Bayesian network approach to model the causal relations among people in social networks, and studied the impact of prior probability, influence strength, and society openness to the inference accuracy on a real online social network.

TL;DR: The result is a high-performance temporal-graph system that offers up to an order of magnitude speedup for temporal iterative graph mining compared to a straightforward application of existing graph engines on a series of snapshots.

TL;DR: This study has made a significant impact on research in physics, computer science and mathematics and given birth to new branches of research in different areas of mathematics, most notably graph theory and probability.

TL;DR: The paper describes the novel technique of categorization by context, which instead extracts useful information for classifying a document from the context where a URL referring to it appears, and presents the results of experimenting with Theseus, a classifier that exploits this technique.

TL;DR: This work proposes and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of \hub pages that join them together in the link structure, that has connections to the eigenvectors of certain matrices associated with the link graph.

TL;DR: In this paper, the authors study in what order a crawler should visit the URLs it has seen, in order to obtain more "important" pages first, and they show that a good ordering scheme can obtain important pages significantly faster than one without.

TL;DR: The authors conclude that older drivers might reduce their accident risk during attention-demanding traffic conditions by avoiding use of cellular phones and that other drivers might do so by refraining from calls involving intense conversation.