This paper proposes a model of network interactions in the interbank market. Our innovation is to model systemic risk in the interbank network as the propagation of incentives or strategic behavior rather than the propagation of losses after default. Transmission in our model is not based on default. Instead, we explain bank profitability based on competition incentives and the outcome of a strategic game. As competitors' lending decisions change, banks adjust their own decisions as a result: generating a “transmission” of shocks through the system. We provide a unique equilibrium characterization of a static model, and embed this model into a full dynamic model of network formation. We also determine the key bank, which is the bank that is crucial for the stability of the financial network.

This paper asks whether the level of integration of world countries in the international network of temporary human mobility can explain differences in their per-capita income and labor productivity. We disentangle the role played by global country centrality in the network from traditional openness measures, which only account for local, nearest-neighbor linkages through which ideas and knowledge can flow. Using 1995-2010 data, we show that global country centrality in the international temporary human-mobility network enhances both per-capita income and labor productivity. Our results hold cross-sectionally, as well as in a dynamic-panel estimation, and take into account potential endogeneity issues. Our findings imply that how close a country is to the theoretical technological frontier, depends not only on how much she is open to temporary human mobility, but mostly on whether she is embedded in a web of relationships connecting her with other influential partners in the network. Our exercises also suggest that most of the gain in income and productivity can be attained if country centrality in the network comes mostly from influential partners that lie not too far away from, but neither too close to them in the network.

Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

Digital technologies have created the potential for new forms of organizing among geographically dispersed individuals by connecting their ideas across the time and space in complex multiteam systems (MTSs). Realizing this potential requires novel forms of shared leadership structures to shepherd divergent and convergent thinking necessary to nurture innovation. While there is limited research on how space influences leadership and how the time influences leadership, there is virtually no theorizing on how space and time interact together to influence the emergence of shared leadership structures that facilitates innovation. A key contribution of this study is to utilize an agent-based model (ABM) that draws upon the research on leadership, networks, and innovation to specify generative mechanisms (or micro-processes) through which shared leadership structures emerge over space and time. The parameters in this model were estimated from empirical data. Results of virtual experiments (VE) yielded testable hypotheses suggesting that, over time, leadership capacity and between-team ties are negatively influenced by space. Furthermore, the computational model suggests that space increases the concentration of divergent leadership but decreases the concentration of convergent leadership. The study concludes by discussing the implications for the design of effective leadership structures to nurture innovation in MTSs.

City road networks have been extensively studied for their social significance or to quantify their connections and centralities, but often their geographical origin is forgotten. This work focuses on the spatial-geographical and geometrical aspects of the road network skeleton. Following previous work, a multi-scale object, the way, is constructed, based only on the local geometry at road crossings. The best method to reconstruct significant elements is investigated. The results show that this object is geographically meaningful, with many particular characteristics. A new indicator, structurality, is introduced and compared with previous indicators, on the cities of Paris and Avignon. Structurality appears to be stable over the borders of the map sample, and is able to reveal the underlying coherence of the road network. This stability can be interpreted as coming from the particular way the network developed in time, and was later preserved. This link with the historical development of the cites, which deserves to be further studied, is exemplified in the cases of Villers-sur-Mer (France) and Manaus (Brazil). The construction method, the results, and their potential meaning are discussed in detail so that they can be used in various related disciplines, such as sociology, town planning, geomatics, and physics.