Betweenness Centrality as a Driver of Preferential Attachment in the Evolution of Research Collaboration Networks

We analyze whether preferential attachment in scientific coauthorship networks is different for authors with different forms of centrality. Using a complete database for the scientific specialty of research about "steel structures," we show that betweenness centrality of an existing node is a significantly better predictor of preferential attachment by new entrants than degree or closeness centrality. During the growth of a network, preferential attachment shifts from (local) degree centrality to betweenness centrality as a global measure. An interpretation is that supervisors of PhD projects and postdocs broker between new entrants and the already existing network, and thus become focal to preferential attachment. Because of this mediation, scholarly networks can be expected to develop differently from networks which are predicated on preferential attachment to nodes with high degree centrality.Comment: Journal of Informetrics (in press

International Research Networks in Pharmaceuticals: Structure and Dynamics

Knowledge production and scientific research have become increasingly more collaborative and international, particularly in pharmaceuticals. We analyze international research networks on the country level in different disease groups. Our empirical analysis is based on a unique dataset of scientific publications related to pharmaceutical research. Using social network analysis, we find that both the number of countries and their connectivity increase in almost all disease groups. The cores of the networks consist of high income OECD countries and remain rather stable over time. We use network regression techniques in order to analyze the dynamics of the networks. Our results indicate that an accumulative advantage based on preferential attachment and point connectivity as a proxy for multi-connectivity are positively related to changes in the countries' collaboration intensity.International Cooperation, Pharmaceuticals, Research Networks, Network Dynamics, MRQAP

Medical device innovation in South Africa: Evolution of collaboration networks (2001-2013)

The evolution of medical device development in South Africa was investigated for the period 2001-2013. Collaboration networks for four sectors - academia, healthcare, industry, and science and support - were derived from a bibliometric study. Centrality measures identified dominant institutions. New actors entering the networks either exhibited preferential attachment to these institutions, or joined the network as part of an isolated cluster. Of the new institutions, foreign collaborators seldom stayed beyond five years, while local institutions seldom left after entering the field. Over the 13-year period, local collaboration activity persisted, while local-foreign collaborations were seen to decline. Over time, the network topology became more akin to that of a small-world network. The findings of the study may support innovation management by guiding institutional strategies for effective collaboration

Hybrid Centrality Measures for Binary and Weighted Networks

Existing centrality measures for social network analysis suggest the im-portance of an actor and give consideration to actor's given structural position in a network. These existing measures suggest specific attribute of an actor (i.e., popularity, accessibility, and brokerage behavior). In this study, we propose new hybrid centrality measures (i.e., Degree-Degree, Degree-Closeness and Degree-Betweenness), by combining existing measures (i.e., degree, closeness and betweenness) with a proposition to better understand the importance of actors in a given network. Generalized set of measures are also proposed for weighted networks. Our analysis of co-authorship networks dataset suggests significant correlation of our proposed new centrality measures (especially weighted networks) than traditional centrality measures with performance of the scholars. Thus, they are useful measures which can be used instead of traditional measures to show prominence of the actors in a network.Comment: a short version accepted in the 3rd workshop on Complex Network [Full Paper submitted to JASIST in April 2011

Optimizing dynamical network structure for pinning control

Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights

The web of federal crimes in Brazil: topology, weaknesses, and control

Law enforcement and intelligence agencies worldwide struggle to find effective ways to fight and control organized crime. However, illegal networks operate outside the law and much of the data collected is classified. Therefore, little is known about criminal networks structure, topological weaknesses, and control. In this contribution we present a unique criminal network of federal crimes in Brazil. We study its structure, its response to different attack strategies, and its controllability. Surprisingly, the network composed of multiple crimes of federal jurisdiction has a giant component, enclosing more than a half of all its edges. This component shows some typical social network characteristics, such as small-worldness and high clustering coefficient, however it is much "darker" than common social networks, having low levels of edge density and network efficiency. On the other side, it has a very high modularity value, $Q=0.96$. Comparing multiple attack strategies, we show that it is possible to disrupt the giant component of the network by removing only $2\%$ of its edges or nodes, according to a module-based prescription, precisely due to its high modularity. Finally, we show that the component is controllable, in the sense of the exact network control theory, by getting access to $20\%$ of the driver nodes.Comment: 9 pages, 5 figure

Establishing and Analyzing the Pattern of Relationships in Co-authorship Networks: the Case Study of Scientific Productions of Researchers at Kerman University of Medical Sciences

Abstract: Introduction: The purpose of this research is to evaluate the co-authorship network of researchers of Kerman University of Medical Sciences. This assessment includes a look at the co-authorship, patterns of co-writing, researchers\u27 outputs, authors ranking, map drawing of the co-authorship network, comparing the network of co-writing of the medical field with other domains, main component and key researchers, review The fit of the network of the co-writing of medical researchers with the small world theory, as well as person-centered indicators such as degree centrality, between centrality, closeness centrality Eigenvector, vector centrality, beta centrality, and interstitial centrality. Method: This research was carried out using scientific methods and network analysis techniques. The statistical population of this research is all articles of the faculty members and other researchers of Kerman University of Medical Sciences, indexed at the ISI database (the Science of Science Web site) during the period from 1978 to 2015, which consists of 1710 articles. The data were analyzed by Bibexcel, Histcite and Net drive softwares after pre-processing. Findings: The review of the articles showed that the pattern of four and five writers had the highest percentage of the co-written articles. The co-authorship network of this university is lower un an index such as the number of papers for each author from many other areas, and in the index of authors for each article is higher than most of the areas. The density index of this network is 0/003, its clustering coefficient is 0/64 and the percentage of the co-written articles in companion with the single-written articles is 97%. The researchers of this university have a high degree of collaboration in writing their articles, Iran University of Medical Sciences , Shahid Bahonar Kerman University and Shahid Beheshti University of Medical Sciences, and the United States, Australia and England have the most scientific cooperation with Kerman University of Medical Sciences. Studies show that most of the articles published at Kerman University of Medical Sciences have been produced by a small number of researchers of this university, and the ratio of national-to-international collaboration at this university has been. 2/9. Conclusion: The co-authorship network of the researchers of this university is characterized by the average length trajectory and relatively high clustering coefficient, which is a small world network. The study of the distribution of the degree centrality of the central and key researchers of the network shows that the principle of success breeds success , which was proposed by Age and Rousseau in 1996, is also valid in the surveyed network, and the researchers with high centrality play a very important role in the development and The evolution of co-writing network