A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

Can co-location be used as a proxy for face-to-face contacts?

Technological advances have led to a strong increase in the number of data collection efforts aimed at measuring co-presence of individuals at different spatial resolutions. It is however unclear how much co-presence data can inform us on actual face-to-face contacts, of particular interest to study the structure of a population in social groups or for use in data-driven models of information or epidemic spreading processes. Here, we address this issue by leveraging data sets containing high resolution face-to-face contacts as well as a coarser spatial localisation of individuals, both temporally resolved, in various contexts. The co-presence and the face-to-face contact temporal networks share a number of structural and statistical features, but the former is (by definition) much denser than the latter. We thus consider several down-sampling methods that generate surrogate contact networks from the co-presence signal and compare them with the real face-to-face data. We show that these surrogate networks reproduce some features of the real data but are only partially able to identify the most central nodes of the face-to-face network. We then address the issue of using such down-sampled co-presence data in data-driven simulations of epidemic processes, and in identifying efficient containment strategies. We show that the performance of the various sampling methods strongly varies depending on context. We discuss the consequences of our results with respect to data collection strategies and methodologies

Locally Adaptive Dynamic Networks

Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with current methods include the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actors' connectivity behaviors. Motivated by this application, we develop a novel method for Locally Adaptive DYnamic (LADY) network inference. The proposed model relies on a dynamic latent space representation in which each actor's position evolves in time via stochastic differential equations. Using a state space representation for these stochastic processes and P\'olya-gamma data augmentation, we develop an efficient MCMC algorithm for posterior inference along with tractable procedures for online updating and forecasting of future networks. We evaluate performance in simulation studies, and consider an application to face-to-face contacts among individuals in a primary school

Disrupting Illicit Supply Networks: New Applications of Operations Research and Data Analytics to End Modern Slavery

Report from a 2017 National Science Foundation workshop on promising research directions for applications of operations research and data analytics toward the disruption of illicit supply networks like human trafficking. The workshop was funded by the NSF’s Operations Engineering (ENG) and the Law & Social Sciences Program (SBE) under grant # CMMI-1726895. The report addresses the opportunity to apply advances from the fields of operations research, management science, analytics, machine learning, and data science toward the development of disruptive interventions against illicit networks. Such an extension of the current research agenda for trafficking would move understanding of such dynamic systems from descriptive characterization and predictive estimation toward improved dynamic operational control.Bureau of Business Researc

Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks

The stochastic block model (SBM) is a flexible probabilistic tool that can be used to model interactions between clusters of nodes in a network. However, it does not account for interactions of time varying intensity between clusters. The extension of the SBM developed in this paper addresses this shortcoming through a temporal partition: assuming interactions between nodes are recorded on fixed-length time intervals, the inference procedure associated with the model we propose allows to cluster simultaneously the nodes of the network and the time intervals. The number of clusters of nodes and of time intervals, as well as the memberships to clusters, are obtained by maximizing an exact integrated complete-data likelihood, relying on a greedy search approach. Experiments on simulated and real data are carried out in order to assess the proposed methodology

Ways to open innovation: main agents and sources in the Portuguese case

Facing increasing open innovation trends, Portuguese enterprises are considering the related processes and impacts. Thus, this work aims to identify the sectors whose enterprises most engage in open innovation (such as cooperation on this issue) and which sources/agents are most used. This is analyzed by sector and type of innovation as an interesting way of differentiation for better open innovation strategy delineation. Using the data from the Community Innovation Survey (CIS-2012), it first appraises the nature of the innovation process, either cooperative or firm-based, as the starting level of analysis. Then, it differentiates the results by sector illustrating which cooperation sources/agents are most used (scope) and relative intensity of use (scale). This is important to assess levels of openness and related factors. Results show that main innovating sectors in Portugal are of three types: research-based, knowledge-based and service-based. They reveal an increasing focus on knowledge and services, trends that have been leading to more active openness towards innovation. For instance, health and construction are increasing their openness for innovating and internationalizing processes. However, Portuguese innovation is still more firm-based (in-house) than cooperation-based, especially concerning new products' launching. This work and future analyzes around it can contribute to encourage the open innovation strategy in more sectors of the economy as an easy and effective way to cope with rapid trends and changes. (C) 2017 Elsevier Ltd. All rights reserved.info:eu-repo/semantics/publishedVersio

Networks and the epidemiology of infectious disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues

The role of supply chain integration in achieving competitive advantage: A study of UK automobile manufacturers

The competitive nature of the global automobile industry has resulted in a battle for efficiency and consistency in supply chain management (SCM). For manufacturers, the diversified network of suppliers represents more than just a production system; it is a strategic asset that must be managed, evaluated, and revised in order to attain competitive advantage. One capability that has become an increasingly essential means of alignment and assessment is supply chain integration (SCI). Through such practices, manufacturers create informational capital that is inimitable, yet transferrable, allowing suppliers to participate in a mutually-beneficial system of performance-centred outcomes. From cost reduction to time improvements to quality control, the benefits of SCI extend throughout the supply chain lifecycle, providing firms with improved predictability, flexibility, and responsiveness. Yet in spite of such benefits, key limitations including exposure to risks, supplier failures, or changing competitive conditions may expose manufacturers to a vulnerable position that can severely impact value and performance. The current study summarizes the perspectives and predictions of managers within the automobile industry in the UK, highlighting a dynamic model of interdependency and interpolation that embraces SCI as a strategic resource. Full commitment to integration is critical to achieving improved outcomes and performance; therefore, firms seeking to integrate throughout their extended supply chain must be willing to embrace a less centralized locus of control