Graph Metrics for Temporal Networks

Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node-node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201

A statistical network analysis of the HIV/AIDS epidemics in Cuba

The Cuban contact-tracing detection system set up in 1986 allowed the reconstruction and analysis of the sexual network underlying the epidemic (5,389 vertices and 4,073 edges, giant component of 2,386 nodes and 3,168 edges), shedding light onto the spread of HIV and the role of contact-tracing. Clustering based on modularity optimization provides a better visualization and understanding of the network, in combination with the study of covariates. The graph has a globally low but heterogeneous density, with clusters of high intraconnectivity but low interconnectivity. Though descriptive, our results pave the way for incorporating structure when studying stochastic SIR epidemics spreading on social networks

Total hip arthroplasty surgical approach does not alter postoperative gait mechanics one year after surgery

Objective: To investigate the differences in gait biomechanics on the basis of surgical approach 1 year after surgery. Design: This was a descriptive laboratory study to investigate the side-to-side differences in walking mechanics at a self-selected walking speed as well as a functional assessment 1year after total hip arthroplasty (THA). Temporospatial, kinetic, and kinematic data as well as functional outcomes were collected. Two-way analysis of variance was used to assess for between-group differences and limb-to-limb asymmetries. Setting: A controlled laboratory study. Participants: This study examined 35 patients with primary, unilateral THA. The THA surgical approaches that were used in these patients included 12 direct lateral, 18 posterior, and 11 anterolateral. All the patients were assessed 1 year after THA. Patients were excluded from the study if they had contralateral hip pain or pathology, or any prior lower extremity total joint replacements. Main Outcome Measurements: Three-dimensional lower extremity kinematics and kinetics as well as spatiotemporal variables were collected. In addition, a series of physical performance measures were collected. Results: No main effects for the physical performance measures or biomechanical variables were observed among the approach groups. Significant limb-to-limb asymmetries were observed among all the patients, with decreased sagittal plane range of motion, peak extension, and peak vertical ground reaction forces on the operative side. Conclusion: The results of this study indicated that no significant differences existed among the different surgical approach groups for any study variable. However, 1 year after THA, the patients demonstrated asymmetric gait patterns regardless of surgical approach, which indicated the potential need for continued intervention through physical therapy to regain normal side-to-side symmetry after THA. © 2014 American Academy of Physical Medicine and Rehabilitation

Wormhole Cosmic Censorship

We analyze the properties of a Kerr-like wormhole supported by phantom matter, which is an exact solution of the Einstein-phantom field equations. It is shown that the solution has a naked ring singularity which is unreachable to null geodesics falling freely from the outside. Similarly to Roger Penrose's cosmic censorship, that states that all naked singularities in the Universe must be protected by event horizons, here we conjecture from our results that a naked singularity can also be fully protected by the intrinsic properties of a wormhole's throat

Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer

© 2020, Springer Nature Switzerland AG. The aim of this paper is to build a computer based clinical decision support tool using a semi-supervised framework, the Fisher Information Network (FIN), for visualization of a set of mammographic images. The FIN organizes the images into a similarity network from which, for any new image, reference images that are closely related can be identified. This enables clinicians to review not just the reference images but also ancillary information e.g. about response to therapy. The Fisher information metric defines a Riemannian space where distances reflect similarity with respect to a given probability distribution. This metric is informed about generative properties of data, and hence assesses the importance of directions in space of parameters. It automatically performs feature relevance detection. This approach focusses on the interpretability of the model from the standpoint of the clinical user. Model predictions were validated using the prevalence of classes in each of the clusters identified by the FIN

From sparse to dense and from assortative to disassortative in online social networks

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.Comment: 10 pages, 7 figures and 2 table

Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure

We consider the edge-based compartmental models for infectious disease spread introduced in Part I. These models allow us to consider standard SIR diseases spreading in random populations. In this paper we show how to handle deviations of the disease or population from the simplistic assumptions of Part I. We allow the population to have structure due to effects such as demographic detail or multiple types of risk behavior the disease to have more complicated natural history. We introduce these modifications in the static network context, though it is straightforward to incorporate them into dynamic networks. We also consider serosorting, which requires using the dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here

Pharmacogenomics in the UK National Health Service: opportunities and challenges

There is increasing interest in pharmacogenomics. However, it is also widely acknowledged that implementation of pharmacogenomics into clinical practice has been slow. Implementation is being undertaken in many centres in the US, but this is not nationwide and often focused on highly specialised academic centres, driven by champions. To date, there has been no implementation on a whole country basis. The UK National Health Service (NHS) is a single integrated healthcare system, which provides free care to all patients at the point of need. Recently, there has been a drive to implement genomic medicine into the NHS, largely spurred on by the success of the 100,000 genomes project. This represents an unprecedented opportunity to implement pharmacogenomics for over 60 million people. In order to discuss the potential for implementing pharmacogenomics into the NHS, the UK Pharmacogenetics and Stratified Medicine Network, NHS England and Genomics England invited experts from academia, the healthcare sector, industry and patient representatives to come together to discuss the opportunities and challenges1. This report highlights the discussions of the workshop with the aim of providing an overview of the issues that need to be considered to enable pharmacogenomic medicine to become mainstream within the NHS