The Complex Network of Evolutionary Computation Authors: an Initial Study

EC paper authors form a complex network of co-authorship which is, by itself, a example of an evolving system with its own rules, concept of fitness, and patterns of attachment. In this paper we explore the network of authors of evolutionary computation papers found in a major bibliographic database. We examine its macroscopic properties, and compare it with other co-authorship networks; the EC co-authorship network yields results in the same ballpark as other networks, but exhibits some distinctive patterns in terms of internal cohesion. We also try to find some hints on what makes an author a sociometric star. Finally, the role of proceeding editorship as the origin of long-range links in the co-authorship network is studied as well.Comment: Sociometric study of the Evolutionary Computation community. Submitted to Evolutionary Computation lette

Managing Opportunities and Challenges of Co-Authorship

Research with the largest impact on practice and science is often conducted by teams with diverse substantive, clinical, and methodological expertise. Team and interdisciplinary research has created authorship groups with varied expertise and expectations. Co-authorship among team members presents many opportunities and challenges. Intentional planning, clear expectations, sensitivity to differing disciplinary perspectives, attention to power differentials, effective communication, timelines, attention to published guidelines, and documentation of progress will contribute to successful co-authorship. Both novice and seasoned authors will find the strategies identified by the Western Journal of Nursing Research Editorial Board useful for building positive co-authorship experiences

Co-authorship networks in Swiss political research

Co-authorship is an important indicator of scientific collaboration. Co-authorship networks are composed of sub-communities, and researchers can gain visibility by connecting these insulated subgroups. This article presents a comprehensive co-authorship network analysis of Swiss political science. Three levels are addressed: disciplinary cohesion and structure at large, communities, and the integrative capacity of individual researchers. The results suggest that collaboration exists across geographical and language borders even though different regions focus on complementary publication strategies. The subfield of public policy and administration has the highest integrative capacity. Co-authorship is a function of several factors, most importantly being in the same subfield. At the individual level, the analysis identifies researchers who belong to the “inner circle” of Swiss political science and who link different communities. In contrast to previous research, the analysis is based on the full set of publications of all political researchers employed in Switzerland in 2013, including past publications

Studying the Emerging Global Brain: Analyzing and Visualizing the Impact of Co-Authorship Teams

This paper introduces a suite of approaches and measures to study the impact of co-authorship teams based on the number of publications and their citations on a local and global scale. In particular, we present a novel weighted graph representation that encodes coupled author-paper networks as a weighted co-authorship graph. This weighted graph representation is applied to a dataset that captures the emergence of a new field of science and comprises 614 papers published by 1,036 unique authors between 1974 and 2004. In order to characterize the properties and evolution of this field we first use four different measures of centrality to identify the impact of authors. A global statistical analysis is performed to characterize the distribution of paper production and paper citations and its correlation with the co-authorship team size. The size of co-authorship clusters over time is examined. Finally, a novel local, author-centered measure based on entropy is applied to determine the global evolution of the field and the identification of the contribution of a single author's impact across all of its co-authorship relations. A visualization of the growth of the weighted co-author network and the results obtained from the statistical analysis indicate a drift towards a more cooperative, global collaboration process as the main drive in the production of scientific knowledge.Comment: 13 pages, 9 figure

Economics of co-authorship

Starting from the literature on the rising incidence of co-authorship in economics, choices about co-authorship are analyzed with a theoretical model, assuming that authors optimize the returns from publications. Results show that co-authorship behavior depends both on the technology of the production of economic research and on the reward system that a researcher faces. Two pay structures are considered, one that is proportional to the number of authors and one that is not. The researchers’ heterogeneity implies a trade-off for the policy maker between the objective of effort maximization and the objective of selection of better researchers. The trade-off is more relevant when low-quality researchers choose to engage in opportunistic behavior to gain from higher-quality collaborations.Co-authorship; Academic research; returns from publications

Overlapping Community Structure in Co-authorship Networks: a Case Study

Community structure is one of the key properties of real-world complex networks. It plays a crucial role in their behaviors and topology. While an important work has been done on the issue of community detection, very little attention has been devoted to the analysis of the community structure. In this paper, we present an extensive investigation of the overlapping community network deduced from a large-scale co-authorship network. The nodes of the overlapping community network represent the functional communities of the co-authorship network, and the links account for the fact that communities share some nodes in the co-authorship network. The comparative evaluation of the topological properties of these two networks shows that they share similar topological properties. These results are very interesting. Indeed, the network of communities seems to be a good representative of the original co-authorship network. With its smaller size, it may be more practical in order to realize various analyses that cannot be performed easily in large-scale real-world networks.Comment: 2014 7th International Conference on u- and e- Service, Science and Technolog

Complete trails of co-authorship network evolution

The rise and fall of a research field is the cumulative outcome of its intrinsic scientific value and social coordination among scientists. The structure of the social component is quantifiable by the social network of researchers linked via co-authorship relations, which can be tracked through digital records. Here, we use such co-authorship data in theoretical physics and study their complete evolutionary trail since inception, with a particular emphasis on the early transient stages. We find that the co-authorship networks evolve through three common major processes in time: the nucleation of small isolated components, the formation of a tree-like giant component through cluster aggregation, and the entanglement of the network by large-scale loops. The giant component is constantly changing yet robust upon link degradations, forming the network's dynamic core. The observed patterns are successfully reproducible through a new network model