The Mathematics of Social Network Analysis: Metrics for Academic Social Networks

Social network analysis plays an important role in analyzing social relations and patterns of interaction among actors in a social network. Such networks can be casual, like those on social media sites, or formal, like academic social networks. Each of these networks is characterised by underlying data which defines various features of the network. Keeping in view the size and diversity of these networks it may not be possible to dissect entire network with conventional means. Social network visualization can be used to graphically represent these networks in a concise and easy to understand manner. Social network visualization tools rely heavily on quantitative features to numerically define various attributes of the network. These features also referred to as social network metrics used everyday mathematics as their foundations. In this paper we provide an overview of various social network analysis metrics that are commonly used to analyse social networks. Explanation of these metrics and their relevance for academic social networks is also outlined

[1]  Viktor K. Prasanna,et al.  Social Networking Analysis: A State of the Art and the Effect of Semantics , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[2]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[4]  Vicente P. Guerrero-Bote,et al.  Quantifying the benefits of international scientific collaboration , 2013, J. Assoc. Inf. Sci. Technol..

[5]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[6]  Soumen Chakrabarti,et al.  Mining the web - discovering knowledge from hypertext data , 2002 .

[7]  Toby E. Stuart Network Positions and Propensities to Collaborate: An Investigation of Strategic Alliance Formation in a High-Technology Industry , 1998 .

[8]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[9]  G. Davis,et al.  Corporate Elite Networks and Governance Changes in the 1980s , 1997, American Journal of Sociology.

[10]  Y. Matsuo,et al.  Extracting a Social Network among Entities by Web mining , 2006 .

[11]  Jeong-Dong Lee,et al.  The scientific impact and partner selection in collaborative research at Korean universities , 2013, Scientometrics.

[12]  Donald de B. Beaver,et al.  Does collaborative research have greater epistemic authority? , 2004, Scientometrics.

[13]  Szu-Hui Wu,et al.  A study of collaborative product commerce by co-citation analysis and social network analysis , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[14]  Narsi Patel Collaboration in the Professional Growth of American Sociology , 1973 .

[15]  K. Brad Wray,et al.  The Epistemic Significance of Collaborative Research , 2002, Philosophy of Science.

[16]  Nian-Shing Chen,et al.  Studying Research Collaboration Patterns via Co-authorship Analysis in the Field of TeL: The Case of Educational Technology & Society Journal , 2014, J. Educ. Technol. Soc..

[17]  Firoozeh Zare-Farashbandi,et al.  Study of co-authorship network of papers in the Journal of Research in Medical Sciences using social network analysis , 2014, Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences.

[18]  Chaomei Chen,et al.  Mining the Web: Discovering knowledge from hypertext data , 2004, J. Assoc. Inf. Sci. Technol..

[19]  Rashid Ali,et al.  Scientific Co-authorship Social Networks: A Case Study of Computer Science Scenario in India , 2012 .

[20]  M. Newman Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.