Evolving Crisis and Changing Networks: NGOs’ Stakeholder Engagement on the Global Refugee Crisis

Nongovernmental organizations (NGOs) increasingly utilize social media for strategic stakeholder engagement. This study proposes a network-oriented theoretical framework to understand how NGOs’ engagement with complex networks of stakeholders on the global refugee issue varies as the issue moves from low to high public attention stages. We draw from research on multistakeholder issue networks and issue niche theory and analyze a large-scale Twitter data set containing tweets from hundreds of organizations from more than 30 countries. This cross-national, longitudinal study tracks issue evolution and NGOs’ tie formation patterns among themselves and with complex stakeholders (i.e., government and media) as public attention to the refugee issue increases. The results of our exponential random graph models (ERGMs) show how cross-sector stakeholders interact dynamically and how different issue identities position NGOs uniquely in issue niches as the issue evolves. We also find that organizations’ country-level homophily influences tie formation. Theoretical and practical implications are discussed.

The Portuguese Hub of Companies in 1973 – Discussing the Corporate Concentration in the Portuguese Economy on the Eve of the April Revolution

The network of Portuguese companies in 1973 has been identified as a relevant element for understanding the economic structure of the country in the decade of 1970–1980. This network had been formed before 1974, during the dictatorship, but it remained after the Carnation Revolution. In spite of such research, this network has not yet been properly analysed, especially through adequate tools from network analysis. This work will detail this network, the different scores of centrality of each company, and their modular structures; it will also discuss estimates from exponential random graph models to identify significant attributes that explain the discovered flows of investment. This work will also detail the processes of vertical integration as well as the specificities of the identified oligopolies.

Measuring reciprocity: Double sampling, concordance, and network construction

Reciprocity—the mutual provisioning of support/goods—is a pervasive feature of social life. Directed networks provide a way to examine the structure of reciprocity in a community. However, measuring social networks involves assumptions about what relationships matter and how to elicit them, which may impact observed reciprocity. In particular, the practice of aggregating multiple sources of data on the same relationship (e.g., “double-sampled” data, where both the “giver” and “receiver” are asked to report on their relationship) may have pronounced impacts on network structure. To investigate these issues, we examine concordance (ties reported by both parties) and reciprocity in a set of directed, double-sampled social support networks. We find low concordance in people’s responses. Taking either the union (including any reported ties) or the intersection (including only concordant ties) of double-sampled relationships results in dramatically higher levels of reciprocity. Using multilevel exponential random graph models of social support networks from 75 villages in India, we show that these changes cannot be fully explained by the increase in the number of ties produced by layer aggregation. Respondents’ tendency to name the same people as both givers and receivers of support plays an important role, but this tendency varies across contexts and relationships type. We argue that no single method should necessarily be seen as the “correct” choice for aggregation of multiple sources of data on a single relationship type. Methods of aggregation should depend on the research question, the context, and the relationship in question.

Networks of Global Social Policy Diffusion: The Effects of Culture, Economy, Colonial Legacies, and Geographic Proximity

The introductory chapter to the volume by Mossig, Windzio, Seitzer and Besche-Truthe defines the core concepts, such as diffusion and contagion, and gives an example of an application diffusion and contagion in epidemiology. The most important underlying functions, namely the logistic density and cumulative logistic density function, are explained, followed by a very brief introduction to the core concepts of event history analysis. In the network diffusion model, contagion, or, in other words, the adoption of information or innovation, is based on the concept of exposure which will be elaborated in this chapter. Finally, after describing and visualizing the four different networks and their correlations, exponential random graph models are used to analyze structural and substantive properties of these networks. The introduction concludes with a brief overview of the chapters.

Testing biological network motif significance with exponential random graph models

Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein–protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.

Physical activity promotion in an urban district: Analyzing the mechanisms of interorganizational cooperation

Past research has identified the importance of cooperation among community-based organizations from different sectors to address public health problems such as insufficient physical activity. However, little is known about how and why interorganizational cooperation occurs. The present study sought to analyze the structure and emergent patterns of interorganizational cooperation within a network promoting physical activity based in an urban district neighborhood of a city in Southwestern Germany. Survey data on cooperative relations among 61 network organizations and organizational attributes (e.g., possession of sport facilities) were collected. Social network analysis was applied to examine network properties and exponential random graph models were estimated to test hypotheses concerning mechanisms and conditions of cooperative tie formation. The results show that the network of cooperation is sparse but characterized by a tendency for cooperation to occur in triangular structures. Other significant mechanisms of cooperative tie formation are preferential attachment, with the community department for education and sports being the most central network actor, and heterophily regarding the cooperation of organizations from different sectors. This study provides valid and reliable findings on conditions of network formation and significant mechanisms of interorganizational cooperation in the field of physical activity promotion. Knowledge about these mechanisms can help to manage networks effectively and efficiently and reveal potentials for improvement and intensification of interorganizational cooperation in both the present and other research areas of health promotion.

Online network monitoring

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.