A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

High resolution dynamical mapping of social interactions with active RFID

In this paper we present an experimental framework to gather data on face-to-face social interactions between individuals, with a high spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess contacts with one another by exchanging low-power radio packets. When individuals wear the beacons as a badge, a persistent radio contact between the RFID devices can be used as a proxy for a social interaction between individuals. We present the results of a pilot study recently performed during a conference, and a subsequent preliminary data analysis, that provides an assessment of our method and highlights its versatility and applicability in many areas concerned with human dynamics

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Burstiness and tie activation strategies in time-varying social networks

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes

Prediction and Topological Models in Neuroscience

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone

Historicising Material Agency: from Relations to Relational Constellations

Relational approaches have gradually been changing the face of archaeology over the last decade: analytically, through formal network analysis; and interpretively, with various frameworks of human-thing relations. Their popularity has been such, however, that it threatens to undermine their relevance. If everyone agrees that we should understand past worlds by tracing relations, then ‘finding relations’ in the past becomes a self-fulfilling prophecy. Focusing primarily on the interpretive approaches of material culture studies, this article proposes to counter the threat of irrelevance by not just tracing human-thing relations, but characterising how sets of relations were ordered. Such ordered sets are termed ‘relational constellations’. The article describes three relational constellations and their consequences based on practices of fine ware production in the Western Roman provinces (first century BC – third century AD): the fluid, the categorical, and the rooted constellation. Specifying relational constellations allows reconnecting material culture to specific historical trajectories, and offers scope for meaningful cross-cultural comparisons. As such a small theoretical addition based on the existing toolbox of practice-based approaches and relational thought can impact on historical narratives, and can save relational frameworks from the danger of triviality.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s10816-015-9244-

Membership nominations in international scientific assessments

International scientific assessments are transnational knowledge-based expert networks with a mandate to advise policymakers. A well-known example is the Millennium Ecosystem Assessment (MA), which synthesized research on ecosystem services between 2001 and 2005, utilizing the knowledge of 1,360 expert members. Little, however, is known about the membership composition and the driving forces behind membership nominations in the MA and similar organizations. Here we introduce a survey data set on recruitment in the MA and analyse nomination patterns among experts as a complex network. The results indicate that membership recruitment was governed by prior contacts in other transnational elite organizations and a range of other factors related to personal affinity. Network analysis demonstrates how some core individuals were particularly influential in shaping the overall membership composition of the group. These findings add to recently noted concerns about the lack of diversity of views represented in international scientific assessments

Understanding the implementation of evidence-based care: A structural network approach

<p>Abstract</p> <p>Background</p> <p>Recent study of complex networks has yielded many new insights into phenomenon such as social networks, the internet, and sexually transmitted infections. The purpose of this analysis is to examine the properties of a network created by the 'co-care' of patients within one region of the Veterans Health Affairs.</p> <p>Methods</p> <p>Data were obtained for all outpatient visits from 1 October 2006 to 30 September 2008 within one large Veterans Integrated Service Network. Types of physician within each clinic were nodes connected by shared patients, with a weighted link representing the number of shared patients between each connected pair. Network metrics calculated included edge weights, node degree, node strength, node coreness, and node betweenness. Log-log plots were used to examine the distribution of these metrics. Sizes of k-core networks were also computed under multiple conditions of node removal.</p> <p>Results</p> <p>There were 4,310,465 encounters by 266,710 shared patients between 722 provider types (nodes) across 41 stations or clinics resulting in 34,390 edges. The number of other nodes to which primary care provider nodes have a connection (172.7) is 42% greater than that of general surgeons and two and one-half times as high as cardiology. The log-log plot of the edge weight distribution appears to be linear in nature, revealing a 'scale-free' characteristic of the network, while the distributions of node degree and node strength are less so. The analysis of the k-core network sizes under increasing removal of primary care nodes shows that about 10 most connected primary care nodes play a critical role in keeping the <it>k</it>-core networks connected, because their removal disintegrates the highest <it>k</it>-core network.</p> <p>Conclusions</p> <p>Delivery of healthcare in a large healthcare system such as that of the US Department of Veterans Affairs (VA) can be represented as a complex network. This network consists of highly connected provider nodes that serve as 'hubs' within the network, and demonstrates some 'scale-free' properties. By using currently available tools to explore its topology, we can explore how the underlying connectivity of such a system affects the behavior of providers, and perhaps leverage that understanding to improve quality and outcomes of care.</p