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Register a new userCollaboration in computer science: a network science approach. Part II
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Added by
Massimo Franceschet
Publication date
2011
fields
Informatics Engineering
and research's language is
English
Authors
Massimo Franceschet
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We represent collaboration of authors in computer science papers in terms of both affiliation and collaboration networks and observe how these networks evolved over time since 1960. We investigate the temporal evolution of bibliometric properties, like size of the discipline, productivity of scholars, and collaboration level in papers, as well as of large-scale network properties, like reachability and average separation distance among scientists, distribution of the number of scholar collaborators, network clustering and network assortativity by number of collaborators.
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Hierarchical domain-specific classification schemas (or subject heading vocabularies) are often used to identify, classify, and disambiguate concepts that occur in scholarly articles. In this work, we develop, apply, and evaluate a human-in-the-loop workflow that first extracts an initial category tree from crowd-sourced Wikipedia data, and then combines community detection, machine learning, and hand-crafted heuristics or rules to prune the initial tree. This work resulted in WikiCSSH; a large-scale, hierarchically organized vocabulary for the domain of computer science (CS). Our evaluation suggests that WikiCSSH outperforms alternative CS vocabularies in terms of vocabulary size as well as the performance of lexicon-based key-phrase extraction from scholarly data. WikiCSSH can further distinguish between coarse-grained versus fine-grained CS concepts. The outlined workflow can serve as a template for building hierarchically-organized subject heading vocabularies for other domains that are covered in Wikipedia.
Several studies exist which use scientific literature for comparing scientific activities (e.g., productivity, and collaboration). In this study, using co-authorship data over the last 40 years, we present the evolutionary dynamics of multi level (i.e., individual, institutional and national) collaboration networks for exploring the emergence of collaborations in the research field of steel structures. The collaboration network of scientists in the field has been analyzed using author affiliations extracted from Scopus between 1970 and 2009. We have studied collaboration distribution networks at the micro-, meso- and macro-levels for the 40 years. We compared and analyzed a number of properties of these networks (i.e., density, centrality measures, the giant component and clustering coefficient) for presenting a longitudinal analysis and statistical validation of the evolutionary dynamics of steel structures collaboration networks. At all levels, the scientific collaborations network structures were central considering the closeness centralization while betweenness and degree centralization were much lower. In general networks density, connectedness, centralization and clustering coefficient were highest in marco-level and decreasing as the network size grow to the lowest in micro-level. We also find that the average distance between countries about two and institutes five and for authors eight meaning that only about eight steps are necessary to get from one randomly chosen author to another.
We study the lobby index (l-index for short) as a local node centrality measure for complex networks. The l-inde is compared with degree (a local measure), betweenness and Eigenvector centralities (two global measures) in the case of biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II). In both networks, the l-index has poor correlation with betweenness but correlates with degree and Eigenvector. Being a local measure, one can take advantage by using the l-index because it carries more information about its neighbors when compared with degree centrality, indeed it requires less time to compute when compared with Eigenvector centrality. Results suggests that l-index produces better results than degree and Eigenvector measures for ranking purposes, becoming suitable as a tool to perform this task.
Conceptually exoplanet research has one foot in the discipline of Astrophysics and the other foot in Planetary Science. Research strategies for exoplanets will require efficient access to data and information from both realms. Astrophysics has a sophisticated, well integrated, distributed information system with archives and data centers which are interlinked with the technical literature via the Astrophysics Data System (ADS). The information system for Planetary Science does not have a central component linking the literature with the observational and theoretical data. Here we propose that the Committee on an Exoplanet Science Strategy recommend that this linkage be built, with the ADS playing the role in Planetary Science which it already plays in Astrophysics. This will require additional resources for the ADS, and the Planetary Data System (PDS), as well as other international collaborators
The recent emergence of online citizen science is illustrative of an efficient and effective means to harness the crowd in order to achieve a range of scientific discoveries. Fundamentally, citizen science projects draw upon crowds of non-expert volunteers to complete short Tasks, which can vary in domain and complexity. However, unlike most human-computational systems, participants in these systems, the `citizen scientists are volunteers, whereby no incentives, financial or otherwise, are offered. Furthermore, encouraged by citizen science platforms such as Zooniverse, online communities have emerged, providing them with an environment to discuss, share ideas, and solve problems. In fact, it is the result of these forums that has enabled a number of scientific discoveries to be made. In this paper we explore the phenomenon of collective intelligence via the relationship between the activities of online citizen science communities and the discovery of scientific knowledge. We perform a cross-project analysis of ten Zooniverse citizen science projects and analyse the behaviour of users with regards to their Task completion activity and participation in discussion and discover collective behaviour amongst highly active users. Whilst our findings have implications for future citizen science design, we also consider the wider implications for understanding collective intelligence research in general.
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