Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

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Models and methods in social network analysis

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading simulating hamiltonian dynamics is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.

Simulating Hamiltonian dynamics.

Continuum percolation: References

This review paper covers the forensic-relevant literature in fingerprint and bodily impression sciences from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20 Review%20 Papers%202019. pdf.

Interpol review of fingermarks and other body impressions 2016-2019.

Efficient sampling algorithm for large-scale optimization under uncertainty problems

http://www.acadiau.ca/~pranjan/research/Ranjan_Spencer_LHS_paper.pdf

Space-filling Latin hypercube designs based on randomization restrictions in factorial experiments

Latin hypercube designs (LHDs) with space-filling properties are widely used for emulating computer simulators. Over the last three decades, a wide spectrum of LHDs have been proposed with space-filling criteria like minimum correlation among factors, maximin interpoint distance, and orthogonality among the factors via orthogonal arrays (OAs). Projective geometric structures like spreads, covers and stars of PG(p-1,q) can be used to characterize the randomization restriction of multistage factorial experiments. These geometric structures can also be used for constructing OAs and nearly OAs (NOAs). In this paper, we present a new class of space-filling LHDs based on NOAs derived from stars of PG(p-1, 2).

Space-filling Latin Hypercube Designs based on Randomization Restrictions in Factorial Experiments

When a latent shoeprint is discovered at a crime scene, forensic analysts inspect it for distinctive patterns of wear such as scratches and holes (known as accidentals) on the source shoe’s sole. If its accidentals correspond to those of a suspect’s shoe, the print can be used as forensic evidence to place the suspect at the crime scene. The strength of this evidence depends on the random match probability—the chance that a shoe chosen at random would match the crime scene print’s accidentals. Evaluating random match probabilities requires an accurate model for the spatial distribution of accidentals on shoe soles. A recent report by the President’s Council of Advisors in Science and Technology criticized existing models in the literature, calling for new empirically validated techniques. We respond to this request with a new spatial point process model (code and synthetic data is available as Supplementary Material) for accidental locations, developed within a hierarchical Bayesian framework. We treat the tread pattern of each shoe as a covariate, allowing us to pool information across large heterogeneous databases of shoes. Existing models ignore this information; our results show that including it leads to significantly better model fit. We demonstrate this by fitting our model to one such database.

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A Bayesian hierarchical model for evaluating forensic footwear evidence

Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node's latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it is well-known that Metropolis within Gibbs is inefficient for large networks; the acceptance ratios are expensive to compute, and the resultant posterior draws are highly correlated. In this article, we propose an alternative Markov chain Monte Carlo strategy---defined using a combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo---that leverages the posterior distribution's functional form for more efficient posterior computation. We demonstrate that these strategies outperform Metropolis within Gibbs and other algorithms on synthetic networks, as well as on real information-sharing networks of teachers and staff in a school district.

Faster MCMC for Gaussian Latent Position Network Models

In typical latent-space network models, nodes have latent positions, which are all drawn independently from a common distribution. As a consequence, the number of edges in a network scales quadratically with the number of nodes, resulting in a dense graph sequence as the number of nodes grows. We propose an adjustment to latent-space network models which allows the number edges to scale linearly with the number of nodes, to scale quadratically, or at any intermediate rate. Our models also form projective families, making statistical inference and prediction well-defined. Built through point processes, our models are related to both the Poisson random connection model and the graphex framework.

Neil Spencer

Papers

Space-filling Latin hypercube designs based on randomization restrictions in factorial experiments

Space-filling Latin Hypercube Designs based on Randomization Restrictions in Factorial Experiments

A Bayesian hierarchical model for evaluating forensic footwear evidence

Faster MCMC for Gaussian Latent Position Network Models

Projective Sparse Latent Space Network Models