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.

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.

Directed network Laplacians and random graph models

We consider spectral methods that uncover hidden structures in directed networks. We establish and exploit connections between node reordering via (a) minimizing an objective function and (b) maximizing the likelihood of a random graph model. We focus on two existing spectral approaches that build and analyse Laplacian-style matrices via the minimization of frustration and trophic incoherence. These algorithms aim to reveal directed periodic and linear hierarchies, respectively. We show that reordering nodes using the two algorithms, or mapping them onto a specified lattice, is associated with new classes of directed random graph models. Using this random graph setting, we are able to compare the two algorithms on a given network and quantify which structure is more likely to be present. We illustrate the approach on synthetic and real networks, and discuss practical implementation issues.

Dyads, triads, and tetrads: a multivariate simulation approach to uncovering network motifs in social graphs

Motifs represent local subgraphs that are overrepresented in networks. Several disciplines document multiple instances in which motifs appear in graphs and provide insight into the structure and processes of these networks. In the current paper, we focus on social networks and examine the prevalence of dyad, triad, and symmetric tetrad motifs among 24 networks that represent six types of social interactions: friendship, legislative co-sponsorship, Twitter messages, advice seeking, email communication, and terrorist collusion. Given that the correct control distribution for detecting motifs is a matter of continuous debate, we propose a novel approach that compares the local patterns of observed networks to random graphs simulated from exponential random graph models. Our proposed technique can produce conditional distributions that control for multiple, lower-level structural patterns simultaneously. We find evidence for five motifs using our approach, including the reciprocated dyad, three triads, and one symmetric tetrad. Results highlight the importance of mutuality, hierarchy, and clustering across multiple social interactions, and provide evidence of “structural signatures” within different genres of graph. Similarities also emerge between our findings and those in other disciplines, such as the preponderance of transitive triads.