%0 Generic %1 citeulike:221098 %A Barabasi, A. %A Jeong, H. %A Neda, Z. %A Ravasz, E. %A Schubert, A. %A Vicsek, T. %D 2001 %K science skillinference socialnets %T Evolution of the social network of scientific collaborations %U http://arxiv.org/abs/cond-mat/0104162 %X The co-authorship network of scientists represents a prototype of complex evolving networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an eight-year period (1991-98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network's time evolution. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically.

@misc{citeulike:221098, abstract = {The co-authorship network of scientists represents a prototype of complex evolving networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an eight-year period (1991-98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network's time evolution. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically.}, added-at = {2006-06-16T10:34:37.000+0200}, author = {Barabasi, A. and Jeong, H. and Neda, Z. and Ravasz, E. and Schubert, A. and Vicsek, T.}, biburl = {https://www.bibsonomy.org/bibtex/2ac3aef3268c3da2ff54d3d96d54dc0c8/ldietz}, citeulike-article-id = {221098}, comment = {the paper examines properties of the network graph of co-authorship. Results: A. Degree distribution follows a power-law B. Average separation decreases in time C. Clustering coefficient decays with time D. Relative size of the largest cluster increases E. Average degree increases F. Node selection is governed by preferential attachment}, eprint = {cond-mat/0104162}, interhash = {6db016ecb3b823dae833883e5e7f141a}, intrahash = {ac3aef3268c3da2ff54d3d96d54dc0c8}, keywords = {science skillinference socialnets}, month = {April}, priority = {0}, timestamp = {2006-06-16T10:34:37.000+0200}, title = {Evolution of the social network of scientific collaborations}, url = {http://arxiv.org/abs/cond-mat/0104162}, year = 2001 }