Community Structure in Jazz

Using a database of jazz recordings we study the collaboration network of jazz musicians. We define the network at two different levels. First we study the collaboration network between individuals, where two musicians are connected if they have played in the same band. Then we consider the collaboration between bands, where two bands are connected if they have a musician in common. The community structure analysis reveals that these constructions capture essential ingredients of the social interactions between jazz musicians. We observe correlations between recording locations, racial segregation and the community structure. A quantitative analysis of the community size distribution reveals a surprising similarity with an e-mail based social network recently studied.Comment: 12 pages, 6 figures, Revtex4 format, Acknowledgments update

Cross‐campus Collaboration: A Scientometric and Network Case Study of Publication Activity Across Two Campuses of a Single Institution

Team science and collaboration have become crucial to addressing key research questions confronting society. Institutions that are spread across multiple geographic locations face additional challenges. To better understand the nature of cross‐campus collaboration within a single institution and the effects of institutional efforts to spark collaboration, we conducted a case study of collaboration at Cornell University using scientometric and network analyses. Results suggest that cross‐campus collaboration is increasingly common, but is accounted for primarily by a relatively small number of departments and individual researchers. Specific researchers involved in many collaborative projects are identified, and their unique characteristics are described. Institutional efforts, such as seed grants and topical retreats, have some effect for researchers who are central in the collaboration network, but were less clearly effective for others

Modelling temporal and spatial features of collaboration network

The collaboration network is an example of a social network which has both non-trivial temporal and spatial dependence. Based on the observations of collaborations in Physical Review Letters, a model of collaboration network is proposed which correctly reproduces the time evolution of the link length distributions, clustering coefficients, degree distributions and assortative property of real data to a large extent.Comment: 8 pages, 10 figures; follow up work on arXiv.org/physics/0511181; accepted for publication in IJMP

Collaboration networks from a large CV database: dynamics, topology and bonus impact

Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions and funding agencies. Here we propose the use of a large and multidisciplinar database of scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. In this database, detailed information about publications and researchers are made available by themselves so that coauthorship is unambiguous and individuals can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors, regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity.We also show that the collaboration network under investigation displays a typical assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike. Finally, our analysis reveals that the distinctive collaboration profile of researchers awarded with governmental scholarships suggests a strong bonus impact on their productivity.Comment: 8 pages, 8 figure

Matching and network effects

The matching of individuals in teams is a key element in the functioning of an economy. The network of social ties can potentially transmit important information on abilities and reputations and also help mitigate matching frictions by facilitating interactions among ¿screened¿ individuals. We conjecture that the probability of i and j forming a team is falling in the distance between i and j in the network of existing social ties. The objective of this paper is to empirically test this conjecture. We examine the formation of coauthor relations among economists over a twenty year period. Our principal finding is that a new collaboration emerges faster among two researchers if they are ¿closer" in the existing coauthor network among economists. This proximity effect on collaboration is strong: being at a network distance of 2 instead of 3, for instance, raises the probability of initiating a collaboration by 27 percent. Research collaboration takes place in an environment where fairly detailed information concerning individual ability and productivity -reflected in publications, employment history, etc.- is publicly available. Our finding that social networks are powerful even in this setting suggests that they must affect matching processes more generally.coauthorship network, matching, network effects, network formation.

Growth and structure of Slovenia's scientific collaboration network

We study the evolution of Slovenia's scientific collaboration network from 1960 till present with a yearly resolution. For each year the network was constructed from publication records of Slovene scientists, whereby two were connected if, up to the given year inclusive, they have coauthored at least one paper together. Starting with no more than 30 scientists with an average of 1.5 collaborators in the year 1960, the network to date consists of 7380 individuals that, on average, have 10.7 collaborators. We show that, in spite of the broad myriad of research fields covered, the networks form "small worlds" and that indeed the average path between any pair of scientists scales logarithmically with size after the largest component becomes large enough. Moreover, we show that the network growth is governed by near-liner preferential attachment, giving rise to a log-normal distribution of collaborators per author, and that the average starting year is roughly inversely proportional to the number of collaborators eventually acquired. Understandably, not all that became active early have till now gathered many collaborators. We also give results for the clustering coefficient and the diameter of the network over time, and compare our conclusions with those reported previously.Comment: 10 pages, 3 figures; accepted for publication in Journal of Informetrics [related work available at http://arxiv.org/abs/1003.1018 and http://www.matjazperc.com/sicris/stats.html