Recent development of network structure analysis shows that it plays an important role in characterizing complex system of many branches of sciences. Different from previous network centrality measures, this paper proposes the notion of topological centrality (TC) reflecting the topological positions of nodes and edges in general networks, and proposes an approach to calculating the topological centrality. The proposed topological centrality is then used to discover communities and build the backbone network. Experiments and applications on research network show the significance of the proposed approach.
Many works are on the collaboration networks and citation networks of scientific research. Most of them focus on the characteristics of collaboration networks. For the structure of social science collaboration network, disciplinary cohesion from @math to @math was studied @cite_2 . The structure of scientific collaboration networks including the shortest paths, weighted networks, and centrality was studied @cite_30 @cite_10 @cite_13 . Coauthor relations were used to study the collaborations between researchers especially the mathematician, and the distribution of relations between papers of Mathematical Review against the number of authors was studied @cite_25 @cite_31 . Relations between researchers were analyzed in Ed "o rs collaboration graph, and the shortest path lengths between researchers were studied @cite_35 .
@cite_30 @cite_13 @cite_35 @cite_2 @cite_31 @cite_10 @cite_25
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In this study, we propose and validate social networks based theoretical model for exploring scholars' collaboration (co-authorship) network properties associated with their citation-based research performance (i.e., g-index). Using structural holes theory, we focus on how a scholar's egocentric network properties of density, efficiency and constraint within the network associate with their scholarly performance. For our analysis, we use publication data of high impact factor journals in the field of ''Information Science & Library Science'' between 2000 and 2009, extracted from Scopus. The resulting database contained 4837 publications reflecting the contributions of 8069 authors. Results from our data analysis suggest that research performance of scholars' is significantly correlated with scholars' ego-network measures. In particular, scholars with more co-authors and those who exhibit higher levels of betweenness centrality (i.e., the extent to which a co-author is between another pair of co-authors) perform better in terms of research (i.e., higher g-index). Furthermore, scholars with efficient collaboration networks who maintain a strong co-authorship relationship with one primary co-author within a group of linked co-authors (i.e., co-authors that have joint publications) perform better than those researchers with many relationships to the same group of linked co-authors. The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). I show that these collaboration networks form “small worlds,” in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied. Abstract Intellectual relationships and collaboration networks are the basis for the development of a knowledge domain. The visual representation of such “knowledge networks” contributes to the overall understanding of intellectual collaborations in a particular knowledge domain. Based on the co-authorship data from recent journal publications over a period of five years, the authors applied social network analysis to explore the network structures and identify their network properties in the hospitality research domain. The analysis revealed the core and peripheral networks where the power law distribution was observed on the pattern of publishing academic papers. The overall network was further examined by nine research streams in both “global” and “contextual” views to understand a broad variety of the collaboration patterns of hospitality researchers. Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis. © 2009 Wiley Periodicals, Inc.