The impact of university patenting on the technological specialization of European regions: a technology-level analysis

This study investigates the relationship between the entry of universities into a new technology field and the innovative activities of firms located in the same geographical area. We aim to assess the presence of a significant correlation between academic research and technological specialization. The empirical setting is based on a dataset of 846,440 patent families, the output of 256 European regions and 428 local universities. The results of the fixed-effect models indicate a robust and positive relationship between the technological entry of academic institutions and the specialization of the region in the same domain. Furthermore, the technological distance between the portfolio of inventions filed by universities and that of co-localized firms is negatively correlated with the subsequent specialization of the hosting region, and this relationship is amplified by the entry of local academies. Several robustness checks have been performed. In particular, the results are tested on sub-samples that distinguish technology fields with lower and higher complexity and geographical regions with lower and higher innovative performance. The technological entry of universities has an additional positive effect for the strong and leading innovators whereas no significant premium or penalty was found for high and low-tech areas. This suggests that the entry of academic institutions into new technology fields occurring in a highly developed innovation ecosystem is more conducive to subsequent industrial specialization thanks to existing collaborations and transmission channels

Theoretical approach and impact of correlations on the critical packet generation rate in traffic dynamics on complex networks

Using the formalism of the biased random walk in random uncorrelated networks with arbitrary degree distributions, we develop theoretical approach to the critical packet generation rate in traffic based on routing strategy with local information. We explain microscopic origins of the transition from the flow to the jammed phase and discuss how the node neighbourhood topology affects the transport capacity in uncorrelated and correlated networks.Comment: 6 pages, 5 figure

Graph Metrics for Temporal Networks

Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node-node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201

Exploratory factor analysis of graphical features for link prediction in social networks

Social Networks attract much attention due to their ability to replicate social interactions at scale. Link prediction, or the assessment of which unconnected nodes are likely to connect in the future, is an interesting but non-trivial research area. Three approaches exist to deal with the link prediction problem: feature-based models, Bayesian probabilistic models, probabilistic relational models. In feature-based methods, graphical features are extracted and used for classification. Usually, these features are subdivided into three feature groups based on their formula. Some formulas are extracted based on neighborhood graph traverse. Accordingly, there exists three groups of features, neighborhood features, path-based features, node-based features. In this paper, we attempt to validate the underlying structure of topological features used in feature-based link prediction. The results of our analysis indicate differing results from the prevailing grouping of these features, which indicates that current literatures\u27 classification of feature groups should be redefined. Thus, the contribution of this work is exploring the factor loading of graphical features in link prediction in social networks. To the best of our knowledge, there is no prior studies had addressed it

Proximity Dimensions and Scientific Collaboration among Academic Institutions in Europe: The Closer, the Better?

The main objective of this paper is to examine the effect of various proximity dimensions (geographical, cognitive, institutional, organizational, social and economic) on academic scientific collaborations (SC). The data to capture SC consists of a set of co-authored articles published between 2006 and 2010 by universities located in EU-15, indexed by the Science Citation Index (SCI Expanded) of the ISI Web of Science database. We link this data to institution-level information provided by the EUMIDA dataset. Our final sample consists of 240,495 co-authored articles from 690 European universities that featured in both datasets. Additionally, we also retrieved data on regional R&D funding from Eurostat. Based on the gravital equation, we estimate several econometrics models using aggregated data from all disciplines as well as separated data for Chemistry & Chemical Engineering, Life Sciences and Physics & Astronomy. Our results provide evidence on the substantial role of geographical, cognitive, institutional, social and economic distance in shaping scientific collaboration, while the effect of organizational proximity seems to be weaker. Some differences on the relevance of these factors arise at discipline level

Understanding the interplay between social and spatial behaviour

According to personality psychology, personality traits determine many aspects of human behaviour. However, validating this insight in large groups has been challenging so far, due to the scarcity of multi-channel data. Here, we focus on the relationship between mobility and social behaviour by analysing trajectories and mobile phone interactions of ∼1000 individuals from two high-resolution longitudinal datasets. We identify a connection between the way in which individuals explore new resources and exploit known assets in the social and spatial spheres. We show that different individuals balance the exploration-exploitation trade-off in different ways and we explain part of the variability in the data by the big five personality traits. We point out that, in both realms, extraversion correlates with the attitude towards exploration and routine diversity, while neuroticism and openness account for the tendency to evolve routine over long time-scales. We find no evidence for the existence of classes of individuals across the spatio-social domains. Our results bridge the fields of human geography, sociology and personality psychology and can help improve current models of mobility and tie formation

Urban road networks -- Spatial networks with universal geometric features? A case study on Germany's largest cities

Urban road networks have distinct geometric properties that are partially determined by their (quasi-) two-dimensional structure. In this work, we study these properties for 20 of the largest German cities. We find that the small-scale geometry of all examined road networks is extremely similar. The object-size distributions of road segments and the resulting cellular structures are characterised by heavy tails. As a specific feature, a large degree of rectangularity is observed in all networks, with link angle distributions approximately described by stretched exponential functions. We present a rigorous statistical analysis of the main geometric characteristics and discuss their mutual interrelationships. Our results demonstrate the fundamental importance of cost-efficiency constraints for in time evolution of urban road networks.Comment: 16 pages; 8 figure

Modern temporal network theory: A colloquium

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.Comment: Final accepted versio

Link-Prediction to Tackle the Boundary Specification Problem in Social Network Surveys

Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups. Hence, a modeling approach is required that extrapolates the locally restricted data to a global network model. We tackle this Missing Data Problem using Link-Prediction techniques from social network research, network generation techniques from the area of Social Simulation, as well as a combination of both. We found that techniques employing less information may be more adequate to solve this problem, especially when data granularity is an issue. We validated the network models created with our techniques on a number of real-world networks, investigating degree distributions as well as the likelihood of links given the geographical distance between two nodes