La dualidad y la agregación de categorías sociales

La "dualidad" es una concepción del enlace micro-macro que implica que elementos correspondientes a dos niveles diferentes de la estructura (tales como personas y grupos, o -como en los datos que analizamos- identidades y prácticas) se co-constituyen mutuamente. Presentamos un algoritmo para la agregación de categorías sociales que son "duales" entre sí. Nuestro algoritmo es aplicable al estudio de datos en tablas de contingencia. Aplicamos nuestro algoritmo en un estudio de la construcción conjunta de identidades sociales (incluyendo etiquetas raciales y étnicas) y prácticas educativas en un contexto universitario."Duality" is a conception of micro-macro linkage that implies that elements at each of two different levels of structure (such as persons and groups, or-as in the data we analyze-identities and practices) co-constitute one another. We present an algorithm for the aggregation of social categories that are "dual" to each other. Our algorithm is applicable to the study of data in contingency tables. We apply our algorithm in a study of the joint construction of social identities (including racial and ethnic labels) and educational practices in a university context

Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions

Business Model Innovation: How Iconic Business Models Emerge

Despite ample research on the topic of business model innovation, little is known about the cognitive processes whereby some innovative business models gain the status of iconic representations of particular types of firms. This study addresses the question: How do iconic business models emerge? In other words: How do innovative business models become prototypical exemplars for new categories of firms? We focus on the case of Airbnb, and analyze how six mainstream business media publications discussed Airbnb between 2008 and 2013. The cognitive process whereby Airbnb’s business model became the iconic business model for the sharing economy involved three phases. First, these publications drew on multiple analogies to try to assimilate Airbnb’s innovative business model into their existing system of categories. Second, they developed a more nuanced understanding of Airbnb’s business model. Finally, they established it as the prototypical exemplar of a new type of organization. We contribute to business model research by providing an elaborated definition of the notion of the iconic business model which is rooted in social categorization research, and by theorizing the cognitive process that underpins the emergence of iconic business models. Our study also complements research on the role of analogical reasoning in business model innovation. Finally, we complement the market categorization literature by documenting a case of the emergence of a prototypical exemplar

Finding and evaluating community structure in networks

We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.Comment: 16 pages, 13 figure

Null Models of Economic Networks: The Case of the World Trade Web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the World Trade Web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950-2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.Comment: 39 pages, 46 figures, 2 table