![]() ![]() Also, the running time is less than several widely used community detection algorithms. We propose the spread sampling algorithm, which can achieve higher community diversity than baselines with fixed sampling budget. Sampling from each community can indeed achieve high community coverage rate, but in most cases, we do not have ground truth communities, and community detection itself is very time-consuming. ![]() Although random walk with teleportation and multiple random walkers can alleviate the problem, they are still inferior to uniform sampling regarding community diversity. Existing link-trace based and random walk based sampling approaches tend to sample from within a community, and hence the sample suffers from the homophily bias. ![]() Hence, to sample from each community can reduce the ``homophily'' of the sample, and achieve a better summary of the networks. It is well known that (social) networks have community structure, and nodes within each community are similar to each other. Node sampling seeks to sample nodes from a network, and one of its applications is to infer the distribution of network statistics (node degree, node label, et cetera.). We show that well-designed sampling methods together with good estimators can not only speed up the inference but also improve the quality. This thesis discusses the design, inference, and applications of both node sampling and edge sampling. Sampling within the network context has been studied in the research community for many years, and has two prevalent branches: node sampling and edge sampling. With limited computation power or time constraint, sampling is usually an essential first step to analyzing large-scale networks. Given the ubiquitousness of the Internet, we are able to collect relational data at an immense scale (Facebook, Twitter, et cetera.). PHOENIX VERSION 8.Networks are an expressive tool to represent relational data in various domains: an email network in a corporate, a co-sponsorship network in Congress, a co-authorship network in academia, et cetera. Regulatory agencies, including the US FDA, Japan Pharmaceutical and Medical Device Agency (PMDA), China Food and Drug Administration (CFDA), and the UK Medicines and Healthcare Products Regulatory Agency (MHRA), all use Phoenix WinNonlin to evaluate drug submissions. It is the industry standard for non-compartmental analysis (NCA), pharmacokinetic/pharmacodynamic (PK/PD), and toxicokinetic (TK) modeling with a proven 30-year history. Phoenix WinNonlin is used by over 10,000 scientists at more than 1,500 establishments in 60 countries. Phoenix WinNonlin™’s integrated tools for data processing, graphing & charting, report generation, and compliance create an efficient, all-in-one collaboration workbench. ![]() PK/PD and non-compartmental analyses can be time consuming, requiring detailed attention to every step from data preparation to report generation. ![]()
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