RESUMEN
An important problem in network analysis is the online detection of anomalous behaviour. In this paper, we introduce a network surveillance method bringing together network modelling and statistical process control. Our approach is to apply multivariate control charts based on exponential smoothing and cumulative sums in order to monitor networks generated by temporal exponential random graph models (TERGM). The latter allows us to account for temporal dependence while simultaneously reducing the number of parameters to be monitored. The performance of the considered charts is evaluated by calculating the average run length and the conditional expected delay for both simulated and real data. To justify the decision of using the TERGM to describe network data, some measures of goodness of fit are inspected. We demonstrate the effectiveness of the proposed approach by an empirical application, monitoring daily flights in the United States to detect anomalous patterns.
RESUMEN
Statistical methods for dynamic network analysis have advanced greatly in the past decade. This article extends current estimation methods for dynamic network logistic regression (DNR) models, a subfamily of the Temporal Exponential-family Random Graph Models, to network panel data which contain missing data in the edge and/or vertex sets. We begin by reviewing DNR inference in the complete data case. We then provide a missing data framework for DNR families akin to that of Little and Rubin (2002) or Gile and Handcock (2010a). We discuss several methods for dealing with missing data, including multiple imputation (MI). We consider the computational complexity of the MI methods in the DNR case and propose a scalable, design-based approach that exploits the simplifying assumptions of DNR. We dub this technique the "complete-case" method. Finally, we examine the performance of this method via a simulation study of induced missingness in two classic network data sets.
RESUMEN
Urban sociologists and criminologists have long been interested in the link between neighborhood isolation and crime. Yet studies have focused predominantly on the internal dimension of social isolation (i.e., increased social disorganization and insufficient jobs and opportunities). This study highlights the need to assess the external dimension of neighborhood isolation, the disconnectedness from other neighborhoods in the city. Analyses of Chicago's neighborhood commuting network over twelve years (2002-2013) showed that violence predicted network isolation. Moreover, pairwise similarity in neighborhood violence predicted commuting ties, supporting homophily expectations. Violence homophily affected tie formation most, while neighborhood violence was important in dissolving ties.
RESUMEN
The renewable energy product trade is critically important to global economic prospects and its rapid development, making it a key issue in international economics of much interest to scholars. Previous studies have paid attention to bilateral trade, yet we still know little about the patterns of renewable energy product trade and its evolution from the whole industry perspective. Based on bilateral trade data, complex network, as well as ERGM and TERGM, we build global renewable energy trade networks (GRETNs) during 2000-2018 and explore the patterns and determinants. The results show that (1) the GRETNs expand during 2000-2018, characterized by a small-world, reciprocity, degree disassortative, and export volume heterogeneity. (2) The GRETNs form four communities, and the community patterns greatly fluctuate over time. (3) Economies in North America, Europe, and Asia play dominant roles, while the USA, Germany, and China are the cores of the GRETNs. (4) Endogenous structure of reciprocity, structural embeddedness, and out-degree popularity are essential parts of the evolving patterns of GRETNs. Most trade relationships are developed between economies located within the same continent, participating in APEC or WTO, or having similar areas. There is heterophily in GDP and per capita income, and Matthew effects in GDP, urbanization, and industrialization rate. Countries that share a common geographic border, language, religion, or currency, being former colonies of the same colonialists, and having signed regional trade agreements are more likely to trade in renewable energy products.