8,008 research outputs found
Applying Science Models for Search
The paper proposes three different kinds of science models as value-added
services that are integrated in the retrieval process to enhance retrieval
quality. The paper discusses the approaches Search Term Recommendation,
Bradfordizing and Author Centrality on a general level and addresses
implementation issues of the models within a real-life retrieval environment.Comment: 14 pages, 3 figures, ISI 201
Science Models as Value-Added Services for Scholarly Information Systems
The paper introduces scholarly Information Retrieval (IR) as a further
dimension that should be considered in the science modeling debate. The IR use
case is seen as a validation model of the adequacy of science models in
representing and predicting structure and dynamics in science. Particular
conceptualizations of scholarly activity and structures in science are used as
value-added search services to improve retrieval quality: a co-word model
depicting the cognitive structure of a field (used for query expansion), the
Bradford law of information concentration, and a model of co-authorship
networks (both used for re-ranking search results). An evaluation of the
retrieval quality when science model driven services are used turned out that
the models proposed actually provide beneficial effects to retrieval quality.
From an IR perspective, the models studied are therefore verified as expressive
conceptualizations of central phenomena in science. Thus, it could be shown
that the IR perspective can significantly contribute to a better understanding
of scholarly structures and activities.Comment: 26 pages, to appear in Scientometric
Static and Dynamic Aspects of Scientific Collaboration Networks
Collaboration networks arise when we map the connections between scientists
which are formed through joint publications. These networks thus display the
social structure of academia, and also allow conclusions about the structure of
scientific knowledge. Using the computer science publication database DBLP, we
compile relations between authors and publications as graphs and proceed with
examining and quantifying collaborative relations with graph-based methods. We
review standard properties of the network and rank authors and publications by
centrality. Additionally, we detect communities with modularity-based
clustering and compare the resulting clusters to a ground-truth based on
conferences and thus topical similarity. In a second part, we are the first to
combine DBLP network data with data from the Dagstuhl Seminars: We investigate
whether seminars of this kind, as social and academic events designed to
connect researchers, leave a visible track in the structure of the
collaboration network. Our results suggest that such single events are not
influential enough to change the network structure significantly. However, the
network structure seems to influence a participant's decision to accept or
decline an invitation.Comment: ASONAM 2012: IEEE/ACM International Conference on Advances in Social
Networks Analysis and Minin
When does centrality matter? Scientific productivity and the moderating role of research specialization and cross-community ties
The present study addresses the ongoing debate concerning academic scientific productivity. Specifically, given the increasing number of collaborations in academia and the crucial role networks play in knowledge creation, we investigate the extent to which building social capital within the academic community represents a valuable resource for a scientist's knowledge-creation process. We measure the social capital in terms of structural position within the academic collaborative network. Furthermore, we analyse the extent to which an academic scientist's research specialization and ties that cross-community boundaries act as moderators of the aforementioned relationship. Empirical results derived from an analysis of an Italian academic community from 2001 to 2008 suggest academic scientists that build social capital by occupying central positions in the community outperform their more isolated colleagues. However, scientific productivity declines beyond a certain threshold value of centrality, hence revealing the existence of an inverted U-shaped relationship. This relationship is negatively moderated by the extent to which an academic focuses research activities in few scientific knowledge domains, whereas it is positively moderated by the number of cross-community ties established
An exploratory social network analysis of academic research networks
For several decades, academics around the world have been collaborating with the view to support the development of their research domain. Having said that, the majority of scientific and technological policies try to encourage the creation of strong inter-related research groups in order to improve the efficiency of research outcomes and subsequently research funding allocation. In this paper, we attempt to highlight and thus, to demonstrate how these collaborative networks are developing in practice. To achieve this, we have developed an automated tool for extracting data about joint article publications and analyzing them from the perspective of social network analysis. In this case study, we have limited data from works published in 2010 by England academic and research institutions. The outcomes of this work can help policy makers in realising the current status of research collaborative networks in England
Development of Computer Science Disciplines - A Social Network Analysis Approach
In contrast to many other scientific disciplines, computer science considers
conference publications. Conferences have the advantage of providing fast
publication of papers and of bringing researchers together to present and
discuss the paper with peers. Previous work on knowledge mapping focused on the
map of all sciences or a particular domain based on ISI published JCR (Journal
Citation Report). Although this data covers most of important journals, it
lacks computer science conference and workshop proceedings. That results in an
imprecise and incomplete analysis of the computer science knowledge. This paper
presents an analysis on the computer science knowledge network constructed from
all types of publications, aiming at providing a complete view of computer
science research. Based on the combination of two important digital libraries
(DBLP and CiteSeerX), we study the knowledge network created at
journal/conference level using citation linkage, to identify the development of
sub-disciplines. We investigate the collaborative and citation behavior of
journals/conferences by analyzing the properties of their co-authorship and
citation subgraphs. The paper draws several important conclusions. First,
conferences constitute social structures that shape the computer science
knowledge. Second, computer science is becoming more interdisciplinary. Third,
experts are the key success factor for sustainability of journals/conferences
Crossâcampus Collaboration: A Scientometric and Network Case Study of Publication Activity Across Two Campuses of a Single Institution
Team science and collaboration have become crucial to addressing key research questions confronting society. Institutions that are spread across multiple geographic locations face additional challenges. To better understand the nature of crossâcampus collaboration within a single institution and the effects of institutional efforts to spark collaboration, we conducted a case study of collaboration at Cornell University using scientometric and network analyses. Results suggest that crossâcampus collaboration is increasingly common, but is accounted for primarily by a relatively small number of departments and individual researchers. Specific researchers involved in many collaborative projects are identified, and their unique characteristics are described. Institutional efforts, such as seed grants and topical retreats, have some effect for researchers who are central in the collaboration network, but were less clearly effective for others
Evaluating Research Output using Scientometric and Social Network Analysis: A Case of Alagappa University, India
This study analyzed the research productivity of Alagappa University (AU), India, in terms of scientometric and social network analysis measures. The primary aim of this study is to construct two types of networks, co-authorship, and citation, with three levels of network measures to divulge the social and intellectual structure of AU and to identify their research hubs, social interactions, the knowledge diffusion pattern, which will help to strengthen their research areas, fund allocation and to formulate appropriate policy strategies. It revealed that AU produced 99.45 % of research articles in collaboration, particularly 88.41% of the articles were the outcome of international scientific collaboration, remaining 11.04% of them have collaborated domestically. It found that the main path of the most cited publications constituted the mainstream of development of the Department of Bio-Technology, AU.https://dorl.net/dor/20.1001.1.20088302.2022.20.2.20.
Exploring Gender Role in Co-Authorship Networks for Computing Books: A Case Study in DBLP
Social network analysis and mining intend to explore for certain, previously unknown, and probably useful relational information from social and information networks. In our case, the research paper is about identifying collaborative networks between the authors (co-authors) of Computer Science books with the highlighted focus on the women computer scientistâs community. Often the hardest part of collaborating is knowing whom you should be collaborating with. Hence, this study will tackle this issue and will identify, and present a visualization of the co-authors which have already collaborated and how often they have collaborated. In this way, we are going to distinguish the successful collaboration between co-authors, the trend of further collaboration between them and the participation of women on these collaborations. This paper is research which is based on detailed and intensive analysis of the different ways of identifying these kinds of connections through secondary material.
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