%0 Generic %1 citeulike:221097 %A Börner, Katy %A Maru, Jeegar T. %A Goldstone, Robert L. %D 2003 %K collaboration_network master_thesis %T The Simultaneous Evolution of Author and Paper Networks %U http://arxiv.org/abs/cond-mat/0311459 %X There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants), as well as computers and algorithms capable of handling this enormous stream of data. This paper reviews major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. We then introduce a general process model that simultaneously grows co-author and paper-citation networks. The statistical and dynamic properties of the networks generated by this model are validated against a 20-year data set of articles published in the Proceedings of the National Academy of Science. Systematic deviations from a power law distribution of citations to papers are well fit by a model that incorporates a partitioning of authors and papers into topics, a bias for authors to cite recent papers, and a tendency for authors to cite papers cited by papers that they have read. In this TARL model (for Topics, Aging, and Recursive Linking), the number of topics is linearly related to the clustering coefficient of the simulated paper citation network.

@misc{citeulike:221097, abstract = {There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants), as well as computers and algorithms capable of handling this enormous stream of data. This paper reviews major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. We then introduce a general process model that simultaneously grows co-author and paper-citation networks. The statistical and dynamic properties of the networks generated by this model are validated against a 20-year data set of articles published in the Proceedings of the National Academy of Science. Systematic deviations from a power law distribution of citations to papers are well fit by a model that incorporates a partitioning of authors and papers into topics, a bias for authors to cite recent papers, and a tendency for authors to cite papers cited by papers that they have read. In this TARL model (for Topics, Aging, and Recursive Linking), the number of topics is linearly related to the clustering coefficient of the simulated paper citation network.}, added-at = {2014-10-01T22:12:04.000+0200}, author = {B{\"o}rner, Katy and Maru, Jeegar T. and Goldstone, Robert L.}, biburl = {https://www.bibsonomy.org/bibtex/2275b56a4cd17f14e737c7076cd0aae4b/subhashpujari}, citeulike-article-id = {221097}, comment = {To our knowledge there exists no algorithmic approach that simultaneously models the evolution of different networks such as co-author and paper citation networks within an ecology of multiple interacting networks. Here we argue that to fully understand the structure, evolution, and utilization of networks, co-author and paper citation networks need to be considered simultaneously. For example, to understand how knowledge diffuses across authors via their papers at the same time that new authors and papers are accumulated; it is essential to model the coupled growth of both network structures.}, description = {They studied the evolution of collaboration and citation network as simultaneous process as an interdependent network which evolve together and affect the evolution process of each other over time. They modeled this simultaneous evolution of these two interdependent network and try to match it.}, eprint = {cond-mat/0311459}, interhash = {d04057831ec4e552c313e30e78bdac63}, intrahash = {275b56a4cd17f14e737c7076cd0aae4b}, keywords = {collaboration_network master_thesis}, month = {November}, priority = {4}, timestamp = {2014-10-01T22:19:43.000+0200}, title = {The Simultaneous Evolution of Author and Paper Networks}, url = {http://arxiv.org/abs/cond-mat/0311459}, year = 2003 }