An integrated examination of collaboration coauthorship networks through structural cohesion, holes, hierarchy, and percolating clusters

Abstract

Structural cohesion, hierarchy, holes, and percolating clusters share a complementary existence in many social networks. Although the individual influences of these attributes on the structure and function of a network have been analyzed in detail, a more accurate picture emerges in proper perspective and context only when research methods are employed to integrate their collective impacts on the network. In a major research project, we have undertaken this examination. This paper presents an extract from this project, using a global network assessment of these characteristics. We apply our methods to analyze the collaboration networks of a subset of researchers in India through their coauthored papers in peer-reviewed journals and conference proceedings in management science, including related areas of information technology and economics. We find the Indian networks to be currently suffering from a high degree of fragmentation, which severely restricts researchers' long-rage connectivities in the networks. Comparisons are made with networks of a similar sample of researchers working in the United States.

Examining the Emergence of Large-scale Structures in Collaboration Networks: Methods in Sociological Analysis

“…The research collaboration scenario studied in this work is realized through the coauthorship network, in which the collaborative activities of researchers culminate in the publishing of one or more scholarly articles in peer-reviewed journals and conference proceedings (Acedo et al 2006;Barabási et al 2002;Ghosh and Kshitij 2014;Ghosh, Kshitij, and Kadyan, 2015;Newman 2001). Although this type of network has been studied for quite some time now, there is rather limited behavioral investigation of cluster evolution in it.…”

“…Of these instances, we consider only those cases where this connection has been formally established through the common presence of these researchers in coauthored papers published in peer-reviewed journals and conference proceedings. A one-mode projective network of this type having size n with dichotomous collaborative association between pairs of researchers is represented by an n  n adjacency matrix A with elements A ij ¼ 1 if researchers i and j are the coauthors of a paper and A ij ¼ 0 otherwise (Barabási et al 2002;Ghosh and Kshitij 2014;Newman 2001). In a more general setting, it is possible to incorporate the strengths of collaborative coupling between pairs of researchers into the network structure (Ghosh et al 2014).…”

“…A one-mode projective network of this type having size n with dichotomous collaborative association between pairs of researchers is represented by an n  n adjacency matrix A with elements A ij ¼ 1 if researchers i and j are the coauthors of a paper and A ij ¼ 0 otherwise (Barabási et al 2002;Ghosh and Kshitij 2014;Newman 2001). In a more general setting, it is possible to incorporate the strengths of collaborative coupling between pairs of researchers into the network structure (Ghosh et al 2014). However, for the primary purpose of investigating the problem of large-scale structure formation in the network, in this work we leave out this additional complication and consider the simpler dichotomous network instead.…”

“…For reference, please see http://www.nlm.nih.gov/pubs/ factsheets/medline.html) database; in astrophysics, it is about 89 percent for the Los Alamos e-Print database; in condensed matter physics, it is about 85 percent for the Los Alamos database; in theoretical high-energy physics, it is approximately 71 percent for the Los Alamos database; in computer science, it is roughly 57 percent for the Networked Computer Science Technical Reference Library database (Newman 2001). MGMT collaboration coauthorship networks in India and the United States have p values lying typically in the range of about 60-70 percent (Ghosh and Kshitij 2014). Collaboration coauthorship networks in cancer research in India have p values lying in the range of 83-96 percent (Kshitij, Ghosh, and Gupta 2015).…”

“…In an analysis of the large-scale structural topology of the MGMT networks, we empirically found the degree distributions to be truncated power laws (Ghosh and Kshitij 2014). To model the present scenario for numerical computations, we implement such a distribution as possessing a general analytical form pðkÞ ¼ k Àa e À k t Li a e À 1 t À Á , where a is the exponent and t is the cutoff of the power law.…”