Maria Prosperina Vitale

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.

Examining the effect of social influence on student performance through network autocorrelation models

The paper investigates the link between student relations and their performances at university. A social influence mechanism is hypothesized as individuals adjusting their own behaviors to those of others with whom they are connected. This contribution explores the effect of peers on a real network formed by a cohort of students enrolled at a graduate level in an Italian University. Specifically, by adopting a network effects model, the relation between interpersonal networks and university performance is evaluated assuming that student performance is related to the performance of the other students belonging to the same group. By controlling for individual covariates, the network results show informal contacts, based on mutual interests and goals, are related to performance, while formal groups formed temporarily by the instructor have no such effect.

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.

Multidimensional Data Analysis to assess interactions in an e-learning community

The quality of the cooperation and collaboration between members is one of the crucial factors in the development of an online learning community. In this paper we focus on the analysis of the quantity and type of interaction and cooperation between students in the asynchronous discussion forum of a virtual classroom. In order to describe both the qualitative and quantitative measures of the interrelationships in the net structure we propose to adopt the theoretical framework of Multidimensional Analysis of Textual Data in connection with the theoretical framework of Social Network Analysis. The tools made available by Correspondence Analysis of the lexical table are used to derive a semantic reference space in which to locate the nodes and arcs of the communication network. The underlying interrelation structure and the evolution of the conversational themes are shown by visualizing the students that share the same vocabulary and patterns of frequent lemmas used in the forum. The role of each student in the communication process is highlighted by suitable statistical indicators defi ned in the framework of Social Network Analysis.

Analysis of Collaborative Patterns in Innovative Networks

This paper focuses on territorial innovative networks, where a variety of actors (firms, institutions and research centers) are involved in research activities, and aims to set up a strategy for the analysis of such networks. The strategy is twofold and relies, on the one hand, on the secondary data available from administrative databases and, on the other, on survey data related to the organizations involved in innovative networks. In order to describe the peculiar structures of innovative networks, the proposed strategy adopts the techniques suggested in the framework of Social Network Analysis. In particular, the main goal of the analysis is to highlight the network characteristics (interactions between industry, university and local government) that can influence network efficiency in terms of knowledge exchange and diffusion of innovation. Our strategy will be discussed in the framework of an Italian technological district, i.e., a type of innovative network.

On the use of external information in social network analysis

Network analysis focuses on links among interacting units (actors). Interactions are often derived from the presence of actors at events or activities (two-mode network) and this information is coded and arranged in a typical affiliation matrix. In addition to the relational data, interest may focus on external information gathered on both actors and events. Our aim is to explore the effect of external information on the formation of ties by setting a strategy able to decompose the original affiliation matrix by linear combinations of data vectors representing external information with a suitable matrix of coefficients. This allows to obtain peculiar relational data matrices that include the effect of external information. The derived adjacency matrices can then be analyzed from the network analysis perspective. In particular, we look for groups of structurally equivalent actors obtained through clustering methods. Illustrative examples and a real dataset in the framework of scientific collaboration will give a major insight into the proposed strategy.

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 Pre-specified Blockmodeling to Analyze Structural Dynamics in Innovation Networks

In recent decades economic theory has highlighted the benefits produced by networks of organizations in fostering innovation. A number of public policies were put in place to favor these innovation networks throughout Europe. The top-down institution of a number of specialized technological districts in Italy has been one of the main outcomes of this new wave of policies, in mid-2000. The aim of this paper is to explore what impact the institution of technological districts had on collaborative patterns over time. Using a pre-specified blockmodeling, observed network configurations obtained by the co-participation to R&D projects undertaken by organizations involved in a technological district are compared with a theoretical core-periphery structure in a 8-years time interval. The analyses of networks over time show that collaborative patterns have evolved from a core-periphery structure towards a complete network in which each research group is connected with the others.

Analyzing Undergraduate Student Graduation Delay: A Longitudinal Perspective

In Italy the number of years in which undergraduate students should complete their education programme is established by law. However, many students obtain their degree after the expected time: a well-known issue affecting numerous Italian universities. In 1999, therefore, the Italian government introduced a reform that, among other aims, intended to reduce the gap between the average number of years in which a student completed the education programme and the official deadline established by the university regulations.

Finite Sample Behavior of MLE in Network Autocorrelation Models

This work evaluates the finite sample behavior of ML estimators in network autocorrelation models, a class of auto-regressive models studying the network effect on a variable of interest. Through an extensive simulation study, we examine the conditions under which these estimators are normally distributed in the case of finite samples. The ML estimators of the autocorrelation parameter have a negative bias and a strongly asymmetric sampling distribution, especially for high values of the network effect size and the network density. In contrast, the estimator of the intercept is positively biased but with an asymmetric sampling distribution. Estimators of the other regression parameters are unbiased, with heavy tails in presence of non-normal errors. This occurs not only in randomly generated networks but also in well-established network structures.