Quantifying dynamical spillover in co-evolving multiplex networks

Multiplex networks (a system of multiple networks that have different types of links but share a common set of nodes) arise naturally in a wide spectrum of fields. Theoretical studies show that in such multiplex networks, correlated edge dynamics between the layers can have a profound effect on dynamical processes. However, how to extract the correlations from real-world systems is an outstanding challenge. Here we provide a null model based on Markov chains to quantify correlations in edge dynamics found in longitudinal data of multiplex networks. We use this approach on two different data sets: the network of trade and alliances between nation states, and the email and co-commit networks between developers of open source software. We establish the existence of "dynamical spillover" showing the correlated formation (or deletion) of edges of different types as the system evolves. The details of the dynamics over time provide insight into potential causal pathways

Collaboration Networks, Structural Holes, And Innovation: A Longitudinal Study

To assess the effects of a firm's network of relations on innovation, this paper elaborates a theoretical framework that relates three aspects of a firm's ego network-direct ties, indirect ties, and structural holes (disconnections between a firm's partners)-to the firm's subsequent innovation output. It posits that direct and indirect ties both have a positive impact on innovation but that the impact of indirect ties is moderated by the number of a firm's direct ties. Structural holes are proposed to have both positive and negative influences on subsequent innovation. Results from a longitudinal study of firms in the international chemicals industry indicate support for the predictions on direct and indirect ties, but in the interfirm collaboration network, increasing structural holes has a negative effect on innovation. Among the implications for interorganizational network theory is that the optimal structure of interfirm networks depends on the objectives of the network members.Managemen

Emergence of nano S&T in Germany : network formation and company performance

This article investigates the emergence of nano S&T in Germany. Using multiple longitudinal data sets, we describe the complete set of research institutions and companies that entered this science-based technology field and the development of their inter-organisational networks between 1991 and 2000. We demonstrate that the co-publication network is a core-periphery structure in which some companies were key players at an early stage of field formation, whereas later universities and other extra-university institutes took over as the central drivers of scientific progress. Further differentiating among types of firms and research organisations, we find that in the co-patent network collaboration is most intense between high-technology firms and use-inspired basic research institutes. While many companies co-patent with several universities or other public institutes, some succeed in establishing almost exclusive relationships with public research units. It is shown that co-patent and co-publication ties are most effective at strengthening the technological performance of firms, that multiple interaction channels increase company performance, and that companies benefit from collaborating with scientifically central universities and institutes. --nanotechnology,network analysis,company performance,public research sector,innovation system,science industry cooperation,Germany

Data-driven modeling of collaboration networks: A cross-domain analysis

We analyze large-scale data sets about collaborations from two different domains: economics, specifically 22.000 R&D alliances between 14.500 firms, and science, specifically 300.000 co-authorship relations between 95.000 scientists. Considering the different domains of the data sets, we address two questions: (a) to what extent do the collaboration networks reconstructed from the data share common structural features, and (b) can their structure be reproduced by the same agent-based model. In our data-driven modeling approach we use aggregated network data to calibrate the probabilities at which agents establish collaborations with either newcomers or established agents. The model is then validated by its ability to reproduce network features not used for calibration, including distributions of degrees, path lengths, local clustering coefficients and sizes of disconnected components. Emphasis is put on comparing domains, but also sub-domains (economic sectors, scientific specializations). Interpreting the link probabilities as strategies for link formation, we find that in R&D collaborations newcomers prefer links with established agents, while in co-authorship relations newcomers prefer links with other newcomers. Our results shed new light on the long-standing question about the role of endogenous and exogenous factors (i.e., different information available to the initiator of a collaboration) in network formation.Comment: 25 pages, 13 figures, 4 table

Quantifying knowledge exchange in R&D networks: A data-driven model

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. This data is used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers which are able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in 8 dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate $\mu$ for an alliance duration $\tau$. Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiency $\mathcal{\hat{C}}_{n}$. This is a new measure, that takes also in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm's position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiency $\mathcal{\hat{C}}_{n}$. Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.Comment: 35 pages, 10 figure

Topics in social network analysis and network science

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided

Governing network evolution in the quest for identity

This paper provides a managerial account of network governance by exploring how initially non-powerful agents, driven by the quest for distinctive identity, shape the governance of their networks over time. The research design is that of a longitudinal comparative case study of the trajectories of three renowned, Oscar-winning Spanish filmmakers. It scrutinizes data coming from original interviews, as well as from multiple secondary data sources, in order to illustrate the propositions advanced. The paper's contribution is sought: 1) in proposing a micro-level framework for systematic thinking about network governance evolution, distinguishing four dimensions (co-governance, structure, strategy, and pace) and their respective sub-categories; 2) in advancing three peculiar identity profiles with different implications for the evolution of network governance (i.e., a maverick, an integrated professional, and a broker); 3) in bringing together two bodies of literature that have not conversed frequently (i.e., network governance and identity) in a largely overlooked cultural and historical context, that of Spain after the transition to democracy in 1975.Network governance; Management

Structural balance emerges and explains performance in risky decision-making.

Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study, we analyze a financial institutional over a two-year period that employed 66 day traders, focusing on links between changes in affective relations and trading performance. Traders' affective relations were inferred from their IMs (>2 million messages) and trading performance was measured from profit and loss statements (>1 million trades). Here, we find that triads of relationships, the building blocks of larger social structures, have a propensity towards affective balance, but one unbalanced configuration resists change. Further, balance is positively related to performance. Traders with balanced networks have the "hot hand", showing streaks of high performance. Research implications focus on how changes in polarization relate to performance and polarized states can depolarize