Global innovation networks are emerging as a result of the international division of innovation processes through, among others, international technological collaborations. At the aggregate level, the creation of technological collaboration between countries can be considered as mutually beneficial (or detrimental) and their random distribution is unlikely. Consequently, the dynamics and evolution of the technological collaborations can be expected to fulfil the criteria of a complex network. To study the structure and evolution of the global technological collaboration network, we use patent-based data of international co-inventions and apply the network analysis. In addition, extending the gravity model of international technological collaboration by measures controlling for countries position in the network, we show that that a country's position in the network has very strong impact on the intensity of collaboration with other members of the network.

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We look at the structure and evolution of an information and communication technology (ICT) global innovation network (GIN) by mapping the locations of R&D centres belonging to a group of multinational ICT enterprises. We found that the number of countries and connections have increased in a very short time, and that most of the newcomers have come from Africa, Asia and South America. We show that a country's network position affects the creation and intensity of R&D linkages with other countries in the network. This suggests that the evolution of the ICT GIN is driven by, among other things, the preferential attachment mechanism, i.e. countries tend to connect to those countries which have more links. A country's position in the network also moderates the effect of standard determinants of innovation i, such as geographic distance. Hence, network position explains the creation of R&D linkages between such distant countries as the US, China or India.

We study the global system of information and communication technology (ICT) research and development (R&D) locations at city level by applying network analysis and profiling R&D locations with respect to technological complexity. We analyse how the position of a city in the network interacts with the level of its technological complexity. The results show that the ownership and location of R&D activities are concentrated. However, cities with high levels of R&D-centre ownership are not necessarily the most important locations of R&D activity. Instead, cities where the corporate control of R&D activities is concentrated play the role of the network hubs. We find that there is a clear relationship between the level of technological complexity and the choice of a city as a location for R&D activity and its role as a network hub. Along with the already established global cities, Chinese cities now occupy key positions in the ICT R&D network.

The aim of this study is to capture a technology's pathway by identifying emerging subdomains in a complex system of economic processes. The objective is to uncover indirect latent relations among agents interacting in a specific techno-economic segment (TES). A methodology, including an "Extract-Transform-Load" (ETL) process preceding the two steps aimed for analysis, is developed to analyse a TES regarding R&D economic processes of the photonics technology. In the first step, economic relevant R&D activities (EU funded projects and patents) are analysed through a multilayer network (MLN) of agents, considering their interactions in three dimensions, which represent occurred and latent relationships: co-participations in economic activities, common geographical location provenance, common use of technological terms. Then communities are detected (Infomap Algorithm for MLN), and their ongoing within and between connections are studied, as potential factors that affect the entire structured technological ecosystem. In the second step, technological subdomains associated with method-oriented and application-oriented activities are identified through topic modelling. Using the MLN structure, the textual information of the corpus of documents describing the aforementioned economic R&D activities is associated to agents, and the topic model (Latent Dirichlet Allocation) uncovers additional potential semantic connections among them. Subsequently, the results of the MLN community detection and of the topic modelling based on the descriptions of economic activities are considered. Hence, the latent relations of agents are mapped.