Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have diff...
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ISBN:
(纸本)9781509019168
Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have different approaches. In this paper, we model a multi-relational scientific social network to assess the impact of content extraction on topological metrics. Thus, a metric composed of topological and semantic approach is proposed to solve link prediction problem. The results were compared with those presented by Katz metric.
The learners' needs are an important factor in designing syllabus and materials design, this research deals with the syllabus and material design based on the professional's needs. It is expected that the syll...
The learners' needs are an important factor in designing syllabus and materials design, this research deals with the syllabus and material design based on the professional's needs. It is expected that the syllabus and materials designes are communicatively applicable to the professional academy. Descriptive method is applied in this research. The sample of this research is 30 students of ATII Immanuel Academy Medan. They were selected by random sampling to get the data, the questioners were administered to students. the questioners consisted of 54 items and the semi structured interview consisted of 5 questions, the finding indicated that learners' needs were focused on reading and speaking skills. With reference appropriately and proportionally derived for students of the Professional Academy. Further on the basis of the syllabus, materials are designed in which the skills of using language become a priority. The results of this research will be disseminated using the website
The human metabolome has remained largely unknown, with most studies annotating ~10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here...
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This research paper examined the connectedness of STEM faculty to others both within and across academic departments who might be potential resources for diffusion of Learner-centered practices, and the impact of part...
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot...
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Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factor...
Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a
Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecula...
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Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small patc...
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ISBN:
(纸本)9781509045518
Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small patches or subgraphs, of the true network, and if there are ambiguities in the identities of nodes or edges in these patches. This may happen because of noise or because of the nature of data;for instance, in social networks, names are typically not unique. Graph assembly refers to the problem of reconstructing a graph from these many, possibly noisy, partial observations. Prior work suggests that graph assembly is essentially impossible in regimes of interest when the true graph is Erdos-Rényi. The purpose of the present paper is to show that a modest amount of clustering is sufficient to assemble even very large graphs. We introduce the G(n,p;q) random graph model, which is the random closure over all open triangles of a G(n,p) Erdos-Rényi, and show that this model exhibits higher clustering than an equivalent Erdos-Rényi . We focus on an extreme case of graph assembly: the patches are small (1-hop egonets) and are unlabeled. We show that in realistic regimes, graph assembly is fundamentally feasible, because we can identify, for every edge e, a subgraph induced by its neighbors that is unique and present in every patch containing e. Using this result, we build a practical algorithm that uses canonical labeling to reconstruct the original graph from noiseless patches. We also provide an achievability result for noisy patches, which are obtained by edge-sampling the original egonets.
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