Domenico De Stefano

The use of different data sources in the analysis of co-authorship networks and scientific performance

Abstract Scientific collaboration is usually derived from archival co-authorship data. Several data sources may be examined, but they all have advantages and disadvantages, especially when a specific discipline or community is of interest. The aim of this paper is to explore the effect of the use of three data sources – Web of Science, Current Index to Statistics and nationally funded research projects – on the analysis of co-authorship networks among Italian academic statisticians. Results provide evidence of our hypotheses on distinct collaboration patterns among statisticians, as well as distinct effects of scientist network positions on scientific performance, by both Statistics subfield and data source.

On the use of Multiple Correspondence Analysis to visually explore affiliation networks

Abstract In this paper we discuss the use of Multiple Correspondence Analysis to analyze and graphically represent two-mode networks, and we propose to apply it in a Greenacres doubling perspective. We discuss how Multiple Correspondence Analysis: (i) properly takes into account the nature of relational data and the intrinsic asymmetry of actors/events in two-mode networks; (ii) allows a proper graphical appraisal of the underlying relational structure of actors or events; (iii) makes it possible to add actor and event attributes to the analysis in order to improve results interpretation; and (iv) gives different results with respect to the usual Simple Correspondence Analysis.

The Analysis of Network Additionality in the Context of Territorial Innovation Policy: The Case of Italian Technological Districts

Evidence from economic literature suggests that innovative activities based on extensive interactions between industry, universities and local government can yield high levels of economic performance. In many countries, therefore, steps have been taken at an institutional level to set up innovation networks and, in particular, regional technological districts. Our paper deals with Italian Technological Districts: we aim to analyse the network additionality for territorial innovation determined by district policy. The analysis is based on a priori structural regional characteristics and on Social Network Analysis techniques.

Modelling Multiple Interactions in Science and Technology Networks

Empirical studies have shown that the extent of innovation diffusion is greatly affected by the structure of the network in which innovation processes take place. This contribution aims to identify the complex structure of relationships at the basis of knowledge and innovation diffusion among actors from various organizations (firms, academic and research institutions) in a given territory. A multiplex approach is proposed to explain co-authorship and co-invention interaction among Author–Inventors community in a specific geographic area. To this end, we carry out a case study of the Trieste area (North-East Italy), characterized by a very high concentration of research organizations and by the emergence of a lively sector of small R&D firms.

Multiple factor analysis for time-varying two-mode networks

Most social networks present complex structures. They can be both multi-modal and multi-relational. In addition, each relationship can be observed across time occasions. Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data structure. In this paper, we adopt an exploratory data analysis point of view, and we present a procedure based on multiple factor analysis and multiple correspondence analysis to deal with time-varying two-mode networks. This procedure allows us to create static displays in order to explore network evolutions and to visually analyze the degree of similarity of actor/event network profiles over time while preserving the different statuses of the two modes.

University-owned and university-invented patents: a network analysis on two Italian universities

The paper presents results from social network analysis applied to data on patenting of academics inventors employed in two Italian universities (Trieste University and Udine university, both located in Friuli Venezia Giulia region). The aim is to compare the co-invention networks generated by the academic inventors, tenured by one of the two universities, in their patenting activity with several organisations—firms, public research organisations—and in their activity for patents owned by one of the two universities. Results show that, despite the structural similarity, non-marginal differences emerge in the interaction of the two forms of patenting across the two universities. Empirical evidence suggests new research questions related in particular to the role played by the differing university patenting strategies in shaping local networks.

Improving co-authorship network structures by combining multiple data sources: evidence from Italian academic statisticians

The aim of the present contribution is to merge bibliographic data for members of a bounded scientific community in order to derive a complete unified archive, with top-international and nationally oriented production, as a new basis to carry out network analysis on a unified co-authorship network. A two-step procedure is used to deal with the identification of duplicate records and the author name disambiguation. Specifically, for the second step we strongly drew inspiration from a well-established unsupervised disambiguation method proposed in the literature following a network-based approach and requiring a restricted set of record attributes. Evidences from Italian academic statisticians were provided by merging data from three bibliographic archives. Non-negligible differences were observed in network results in the comparison of disambiguated and not disambiguated data sets, especially in network measures at individual level.

A Comparison of χ2 Metrics for the Assessment of Relational Similarities in Affiliation Networks

Factorial techniques are widely used in Social Network Analysis to analyze and visualize networks. When the purpose is to represent the relational similarities, simple correspondence analysis is the most frequent used technique. However, in the case of affiliation networks, its use can be criticized because the involved χ 2 distance does not adequately reflect the actual relational patterns. In this paper we perform a simulation study to compare the metric involved in Correspondence Analysis with respect to the one in Multiple Correspondence Analysis. Analytical results and simulation outcomes show that Multiple Correspondence Analysis allows a proper graphical appraisal of the underlying two-mode relational structure.

Improving Co-authorship Network Structures by Combining Heterogeneous Data Sources

The present paper aims at describing the scientific collaboration patterns of the Italian academic statisticians by merging bibliographic data from heterogenous sources–ISI-WoS, Current Index to Statistics, and the database of nationally funded research projects, PRIN. To obtain a unified database, containing both top international as well as nationally oriented production, information were combined by identifying and linking duplicate records, i.e. record linkage. The unique co-authorship network was then used as basis for network analysis.

Analysis of Collaboration Structures Through Time: The Case of Technological Districts

In the present work we propose to analyze, through Multiple Factorial Analysis (MFA), the relational structure embedded in collaboration networks observed across time occasions. We show, through a case study, how the solutions provided by the MFA can be interpreted in a suitable way in the relational setting which arises in complex and heterogeneous networks. Valuable information about the strength and typology of the collaboration structure and its evolution can be obtained. As case study, we analyze a Technological District located in South Italy.