Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Graph-based ranking algorithms for e-mail expertise analysis

Abstract: In this paper we study graph--based ranking measures for the purpose of using them to rank email correspondents according to their degree of expertise on subjects of interest. While this complete expertise analysis consists of several steps, in this paper we focus on the analysis of digraphs whose nodes correspond to correspondents (people), whose edges correspond to the existence of email correspondence between the people corresponding to the nodes they connect and whose edge directions point from the member of the pair whose relative expertise has been estimated to be higher. We perform our analysis on both synthetic and real data and we introduce a new error measure for comparing ranked lists.

The Future of Journalism

Abstract: Hawaiian Sumo wrestlers Akebono and Konishiki had taken the top two rankings in a sport that had never seen a non-Japanese champion. Akebono was the first to reach Yokozuna, the highest rank. Konishiki, nicknamed “The Dump Truck,” is Ozeki, the second-highest rank. Both men wrestled their titles from the status quo. While the hierarchy of Sumo wrestling may not have broad in fluence in a global sense, the namesakes of these two champions would change the world .

Differential diagnosis for bilateral abnormalities of the basal ganglia and thalamus.

Abstract: The basal ganglia and thalamus are paired deep gray matter structures that may be involved by a wide variety of disease entities. The basal ganglia are highly metabolically active and are symmetrically affected in toxic poisoning, metabolic abnormalities, and neurodegeneration with brain iron accumulation. Both the basal ganglia and thalamus may be affected by other systemic or metabolic disease, degenerative disease, and vascular conditions. Focal flavivirus infections, toxoplasmosis, and primary central nervous system lymphoma may also involve both deep gray matter structures. The thalamus is more typically affected alone by focal conditions than by systemic disease. Radiologists may detect bilateral abnormalities of the basal ganglia and thalamus in different acute and chronic clinical situations, and although magnetic resonance (MR) imaging is the modality of choice for evaluation, the correct diagnosis can be made only by taking all relevant clinical and laboratory information into account. The neuroimaging diagnosis is influenced not only by detection of specific MR imaging features such as restricted diffusion and the presence of hemorrhage, but also by detection of abnormalities involving other parts of the brain, especially the cerebral cortex, brainstem, and white matter. Judicious use of confirmatory neuroimaging investigations, especially diffusion-weighted imaging, MR angiography, MR venography, and MR spectroscopy during the same examination, may help improve characterization of these abnormalities and help narrow the differential diagnosis.

Sparsification of influence networks

Abstract: We present Spine, an efficient algorithm for finding the "backbone" of an influence network. Given a social graph and a log of past propagations, we build an instance of the independent-cascade model that describes the propagations. We aim at reducing the complexity of that model, while preserving most of its accuracy in describing the data.We show that the problem is inapproximable and we present an optimal, dynamic-programming algorithm, whose search space, albeit exponential, is typically much smaller than that of the brute force, exhaustive-search approach. Seeking a practical, scalable approach to sparsification, we devise Spine, a greedy, efficient algorithm with practically little compromise in quality.We claim that sparsification is a fundamental data-reduction operation with many applications, ranging from visualization to exploratory and descriptive data analysis. As a proof of concept, we use Spine on real-world datasets, revealing the backbone of their influence-propagation networks. Moreover, we apply Spine as a pre-processing step for the influence-maximization problem, showing that computations on sparsified models give up little accuracy, but yield significant improvements in terms of scalability.

Prevalence of internet addiction and comparison of internet addicts and non-addicts in Iranian high schools.

Abstract: In this study, 1968 high-school students were selected randomly through clustering, who responded to the Persian version of four measures: the Internet Addiction Test (IAT), UCLA Loneliness Scale, Rosenberg Self-Esteem Scale, and Matson Evaluation of Social Skills. Of the sample, 977 students were Internet users, who were classified into 37 Internet addicts, 304 possible Internet addicts, and 636 moderate users. Since possible addicts, moderate users, and nonusers can all be considered nonaddicts, to make a comprehensive and controlled comparison between addicts and nonaddicts, 37 possible addicts, 37 moderate users and 37 nonusers were matched with the Internet addicts. Results suggest that Internet addicts are lonelier and have lower self-esteem and poorer social skills than moderate users, but not necessarily than possible addicts or nonusers.