Geographic clustering and network evolution of innovative activities: Evidence from China’s patents

This study examines the spatial distribution and social structure of processes of learning and knowledge creation within the context of the inventor network connecting Chinese patent teams. Results uncover mixed tendencies toward both geographic co-location and dispersion arising from combined processes of intra-cluster learning and extra-cluster networking. These processes unfold within a social network that becomes less fragmented over time: as a giant component emerges and increases in size, social distances among inventors become longer. The interplay between geographic and network proximity is assessed against China’s institutional environment. Implications of the findings are discussed for regional development and policy-making.clusters; knowledge transfer; social networks; patenting

Modelling Sociality in the BDI Framework

Postprin

What Does Network Analysis Teach Us about International Environmental Cooperation?

This paper uses network analysis to study the structural properties of international environmental cooperation. We investigate four pertinent hypotheses. First, we quantify how the growing popularity of environmental treaties since the early 1970s has led to the emergence of an environmental collaboration network and document how collaboration is accelerating. Second, we show how over time the network has become denser and more cohesive, and distances between countries have become shorter, facilitating more effective policy coordination and knowledge diffusion. Third, we find that the network, while global, has a noticeable European imprint: initially, the United Kingdom and more recently France and Germany have been the most important players to broker environmental cooperation. Fourth, international environmental coordination started with fisheries and the sea but is now most intense on waste and hazardous substances. The network of air and atmosphere treaties has distinctive topological features, lacks the hierarchical organization of other networks, and is the network most significantly shaped by UN-sponsored treaties

What does network analysis teach us about international environmental cooperation?

This paper uses network analysis to study the structural properties of international environmental cooperation. We investigate four pertinent hypotheses. First, we quantify how the growing popularity of environmental treaties since the early 1970s has led to the emergence of an environmental collaboration network and document how collaboration is accelerating. Second, we show how over time the network has become denser and more cohesive, and distances between countries have become shorter, facilitating more effective policy coordination and knowledge diffusion. Third, we find that the network, while global, has a noticeable European imprint: initially, the United Kingdom and more recently France and Germany have been the most important players to broker environmental cooperation. Fourth, international environmental coordination started with fisheries and the sea but is now most intense on waste and hazardous substances. The network of air and atmosphere treaties has distinctive topological features, lacks the hierarchical organization of other networks, and is the network most significantly shaped by UN-sponsored treaties

A unifying framework for measuring weighted rich clubs.

Network analysis can help uncover meaningful regularities in the organization of complex systems. Among these, rich clubs are a functionally important property of a variety of social, technological and biological networks. Rich clubs emerge when nodes that are somehow prominent or 'rich' (e.g., highly connected) interact preferentially with one another. The identification of rich clubs is non-trivial, especially in weighted networks, and to this end multiple distinct metrics have been proposed. Here we describe a unifying framework for detecting rich clubs which intuitively generalizes various metrics into a single integrated method. This generalization rests upon the explicit incorporation of randomized control networks into the measurement process. We apply this framework to real-life examples, and show that, depending on the selection of randomized controls, different kinds of rich-club structures can be detected, such as topological and weighted rich clubs.J.A. is supported by the NIH-Oxford-Cambridge Scholarship Program. P.P. is employed by Queen Mary University of London. M.R. is supported by the NARSAD Young Investigator and Isaac Newton Trust grants. E.T.B. is employed half-time by the University of Cambridge, UK, and half-time by GlaxoSmithKline (GSK). P.E.V. is supported by the Medical Research Council (grant number MR/K020706/1).This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/srep0725

How Online Communities of People With Long-Term Conditions Function and Evolve: Network Analysis of the Structure and Dynamics of the Asthma UK and British Lung Foundation Online Communities.

BACKGROUND: Self-management support can improve health and reduce health care utilization by people with long-term conditions. Online communities for people with long-term conditions have the potential to influence health, usage of health care resources, and facilitate illness self-management. Only recently, however, has evidence been reported on how such communities function and evolve, and how they support self-management of long-term conditions in practice. OBJECTIVE: The aim of this study is to gain a better understanding of the mechanisms underlying online self-management support systems by analyzing the structure and dynamics of the networks connecting users who write posts over time. METHODS: We conducted a longitudinal network analysis of anonymized data from 2 patients' online communities from the United Kingdom: the Asthma UK and the British Lung Foundation (BLF) communities in 2006-2016 and 2012-2016, respectively. RESULTS: The number of users and activity grew steadily over time, reaching 3345 users and 32,780 posts in the Asthma UK community, and 19,837 users and 875,151 posts in the BLF community. People who wrote posts in the Asthma UK forum tended to write at an interval of 1-20 days and six months, while those in the BLF community wrote at an interval of two days. In both communities, most pairs of users could reach one another either directly or indirectly through other users. Those who wrote a disproportionally large number of posts (the superusers) represented 1% of the overall population of both Asthma UK and BLF communities and accounted for 32% and 49% of the posts, respectively. Sensitivity analysis showed that the removal of superusers would cause the communities to collapse. Thus, interactions were held together by very few superusers, who posted frequently and regularly, 65% of them at least every 1.7 days in the BLF community and 70% every 3.1 days in the Asthma UK community. Their posting activity indirectly facilitated tie formation between other users. Superusers were a constantly available resource, with a mean of 80 and 20 superusers active at any one time in the BLF and Asthma UK communities, respectively. Over time, the more active users became, the more likely they were to reply to other users' posts rather than to write new ones, shifting from a help-seeking to a help-giving role. This might suggest that superusers were more likely to provide than to seek advice. CONCLUSIONS: In this study, we uncover key structural properties related to the way users interact and sustain online health communities. Superusers' engagement plays a fundamental sustaining role and deserves research attention. Further studies are needed to explore network determinants of the effectiveness of online engagement concerning health-related outcomes. In resource-constrained health care systems, scaling up online communities may offer a potentially accessible, wide-reaching and cost-effective intervention facilitating greater levels of self-management

Degree correlations in signed social networks

We investigate degree correlations in two online social networks where users are connected through different types of links. We find that, while subnetworks in which links have a positive connotation, such as endorsement and trust, are characterized by assortative mixing by degree, networks in which links have a negative connotation, such as disapproval and distrust, are characterized by disassortative patterns. We introduce a class of simple theoretical models to analyze the interplay between network topology and the superimposed structure based on the sign of links. Results uncover the conditions that underpin the emergence of the patterns observed in the data, namely the assortativity of positive subnetworks and the disassortativity of negative ones. We discuss the implications of our study for the analysis of signed complex networks

Multiplex PageRank

(15 pages, 6 figures