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Trends in science networks: understanding structures and statistics of scientific networks

Trends in science networks: understanding structures and statistics of scientific networks The growing of availability of electronic resources over the Internet enables rapid dissemination of the ideas and changes in the trends and the interaction patterns. In this work, we focus on dynamic, evolving social networks which exhibit numerous features that are also of interest to many researchers in non-social fields such as statistical physics, biology, applied mathematics, and computer science. We investigate how a specific research area (high-energy physics) changes over time, by building complex, interlinked citation, publication, and co-publication networks that evolve and expand constantly through the emergence of new papers and authors. Following an interdisciplinary approach, we perform a wide-ranging analysis of the high-energy physics dataset using techniques such as social networks centrality analysis, topological analysis, investigation of power law characteristics, time series analysis of publication and collaboration frequencies, as well as spatiotemporal analysis to discuss relationships among involved countries. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

Trends in science networks: understanding structures and statistics of scientific networks

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Publisher
Springer Journals
Copyright
Copyright © 2012 by Springer-Verlag
Subject
Computer Science; Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Methodology of the Social Sciences
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-011-0044-6
Publisher site
See Article on Publisher Site

Abstract

The growing of availability of electronic resources over the Internet enables rapid dissemination of the ideas and changes in the trends and the interaction patterns. In this work, we focus on dynamic, evolving social networks which exhibit numerous features that are also of interest to many researchers in non-social fields such as statistical physics, biology, applied mathematics, and computer science. We investigate how a specific research area (high-energy physics) changes over time, by building complex, interlinked citation, publication, and co-publication networks that evolve and expand constantly through the emergence of new papers and authors. Following an interdisciplinary approach, we perform a wide-ranging analysis of the high-energy physics dataset using techniques such as social networks centrality analysis, topological analysis, investigation of power law characteristics, time series analysis of publication and collaboration frequencies, as well as spatiotemporal analysis to discuss relationships among involved countries.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Jan 6, 2012

References