Territorial Patterns of Innovation in Europe

This paper investigates over the way in which regions innovate. The conceptual framework departs from the simple idea that scientific activities equates knowledge, assuming that the presence of local knowledge produced by research centers, universities and firms was a necessary and sufficient condition for increasing the innovative capacities in local firms, fed by local spillovers. In particular, the paradigmatic jump in interpreting regional innovation processes lies in a conceptual framework interpreting not a single phase of the innovation process, but the different modes of performing the different phases of the innovation process, highlighting the context conditions (internal and external to the region) that accompany each innovation pattern. The paper conceptually identifies different territorial patterns of innovation, and empirically test their existence in Europe. Interesting results emerge from the European territory, witnessing the existence of large differences in the territorial patterns of innovation. These results strongly support normative suggestions towards thematically/regionally focused innovation policies.

Beyond clusters: Fostering innovation through a differentiated and combined network approach

Over the past decades, economic and innovation policy across Europe moved in the direction of creating regional clusters of related firms and institutions. Creating clusters through public policy is risky, complex and costly, however. Moreover, it is not necessary to rely on clusters to stimulate innovation. A differentiated and combined network approach to enhancing innovation and stimulating economic growth may be more efficient and effective, especially though not exclusively in regions lacking clusters. The challenge of such a policy is to mitigate the bottlenecks associated with ‘global pipeline’, ‘local buzz’ and ‘stand alone’ strategies used by innovative firms (cf. Bathelt et al. 2004; Atzema & Visser 2005b), and to combine these strategies with a view to their complementarity in terms of knowledge effects. Private and semi-public brokers will be key in the evolving policy, as timely organizational change is crucial for continued innovation, while brokers also need to mitigate governance problems. This requires region-specific knowledge in terms of sectors, life cycles, institutional and socio-cultural factors, and yields spatially differentiated and differentiating adjustment strategies. The role of public policy is to assist in recruiting, provide start-up funding and monitor brokers. With this, policy moves towards a decentralized, process-based, region-specific, spatially diverging and multi-level system of innovation that is geared towards the evolving innovation strategies of firms.innovation policy, clusters, networks, governance, regionalization

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Learning predictive categories using lifted relational neural networks

Lifted relational neural networks (LRNNs) are a flexible neural-symbolic framework based on the idea of lifted modelling. In this paper we show how LRNNs can be easily used to specify declaratively and solve learning problems in which latent categories of entities, properties and relations need to be jointly induced

The role of internationalization as a determinant of innovation performance: an analysis of 42 countries

This paper analyses the impact of internationalization on the innovation performance of 42 countries. Innovation performance – the dependent variable – is measured by the number of triad patents and PCT applications that originate from a country. The following internationalization variables – independent variables – are used: inward and outward stock of FDI, exports and imports as well as the number of parent companies in a country. Information on patents and the internationalization variables, together with further explanatory variables, including the number of scientific articles in a country, the number of Internet users, the R&D intensity and the share of value added in services, are collected for the years 1990 to 2008. Regressions are performed for all countries together, and, then, for two groups of countries clustered on the basis of their GDP per capita. We estimate two linear models, one based on pooled data estimating the classic linear model, and one on panel data, estimating a fixed effects linear model. The values of our dependent variables lead by up to six years for two reasons: to account for the time that elapses between an invention and the recording of the patent statistic, and, to address at least to some extent, issues associated with endogeneity in our independent variables. The paper finds support for a positive impact of internationalization on countries’ innovation performance. Our analyses suggest that competing in international markets via outward FDI and exports increases the scope of learning and the need to innovate. We find evidence of a negative relationship between patenting and inward FDI as well as imports. We interpret our results to indicate that (a) the inward inflow of investment or products can be less innovation-intensive than a country’s domestic activities which would be the case for more advanced and innovation-active countries; or (b) that a country does not have a sufficient absorption capacity to benefit from inflows

The ‘de-territorialisation of closeness’ - a typology of international successful R&D projects involving cultural and geographic proximity

Although there is a considerable amount of empirical evidence on inter-firm collaborations within technology-based industries, there are only a few works concerned with R&D cooperation by low-tech firms, especially SMEs. Providing further and new evidence based on a recently built database of CRAFT projects, this study analyzes the relationship between technology and proximity in international R&D networks using Homogeneity Analysis by Means of Alternating Least Squares (HOMALS) and statistical cluster techniques. The resulting typology of international cooperative R&D projects highlights that successful international cooperative R&D projects are both culturally/geographically closer and distant. Moreover, and quite interestingly, geographically distant projects are technologically more advanced whereas those located near each other are essentially low tech. Such evidence is likely to reflect the tacit-codified knowledge debate boosted recently by the ICT “revolution” emphasized by the prophets of the “Death of Distance” and the “End of Geography”.Research and Development (R&D); proximity; SMEs

Structural Logistic Regression for Link Analysis

We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in relational databases in which potential predictors, both boolean and real-valued, are generated by structured search in the space of queries to the database, and then tested with statistical information criteria for inclusion in a logistic regression. Using statistics and relational representation allows modeling in noisy domains with complex structure. Link prediction is a task of high interest with exactly such characteristics. Be it in the domain of scientific citations, social networks or hypertext, the underlying data are extremely noisy and the features useful for prediction are not readily available in a flat file format. We propose the application of Structural Logistic Regression to building link prediction models, and present experimental results for the task of predicting citations made in scientific literature using relational data taken from the CiteSeer search engine. This data includes the citation graph, authorship and publication venues of papers, as well as their word content