TARL (Topics, Aging and Recursive Linking) is a general process model de= veloped by K. B=C3=B6rner, J. Maru and R. L. Goldstone which models the sim= ultaneous evolution of author and paper networks. The model attempts to cap= ture the roles of authors and papers in the production, storage and dissemi= nation of knowledge. Information diffusion is assumed to occur directly via= co-authorship and indirectly via the consumption of other author's papers.= The model generates a bipartite evolving network which also incorporates a= ging in the paper citation network.

=20The model uses the simplifying assumption that there is a single level o= f specific topics. A fixed number of authors are generated each year. The s= et of authors are interlinked via undirected coauthorship relations. Papers= are generated by these authors when they find a sufficient number of coaut= hors in their topic. The number of coauthors for a particular paper is also= kept fixed for simplification. Authors "consume" papers before they "produ= ce" a new paper. So authors and papers are linked via directed "consumed" l= inks denoting the information flow from paper to author and directed "produ= ced" links when a new paper is generated by these authors. The in-degree of= a paper node refers to the number of references and the out-degree to its = number of recieved citations. Some other simplifying assumptions that the m= odel has are the following. Each author generates a fixed number of papers = each year. Each paper has a fixed number of references. Each author and eac= h paper have a fixed topic.

=20The model starts with an initial number of authors assigned to a fixed n= umber of topics randomly. At every timestep a fixed number of authors are a= dded. After certain years a fixed number of authors are deleted and are una= ble to coauthor with new authors due the finite lifespan of authors. Papers= though once generated can be cited at any time. However to reflect the fac= t that more recent papers are being cited, the probability of citing a pape= r is modeled by a Weibull distribution. The Weibull distribution is over ti= me and as age increases the probability of citing a paper goes down. So ver= y old papers tend to get very few or no citations since they are in the tai= l of the distribution.

=20The model was successfully validated by a 20 year (1982-2001) data set o= f articles published in the PNAS (Proceedings of the National Academy of Sc= iences).

=20The model can be used to generate bipartite networks of coevolving autho= rs and papers. It can be applied to other datasets with different aging dis= tribution.

=20The model has been implemented in Java. There are two input files which = are provided. On clicking the TARL model, you have to choose these files. T= he files can be found in the directory /sampledata/Network/TARL. They are i= niscript.tarl and agingfunction.txt.

=20The iniscript.tarl contains all the input parameters. The model can be s= tarted with or without topics. The first line of iniscript.tarl indicates t= hat. Aging can either be enabled by setting the counter to 1 or disabled by= setting the counter to zero. Then the following parameters have to be supp= lied. StartYear, EndYear, number of authors in StartYear, number of papers = in StartYear, the maximum age of authors, the number of topics, number of a= uthors to be deleted per 10 year(s), growth in number of authors per 1Year(= s), number of publications per year, number of co-authors, number of papers= read each year, number of papers cited each year, number of papers produce= d each year and the number of references considered.

=20The second file is the agingfunction.txt. This file gives a probability = distribution that the program reads. The file consists of a single column (= the dependent or y values). The independent (or x) axis is considered to be= the age which starts from 0 and goes to 21. The distribution is a Weibull = distribution with shape parameter 2 and scale parameter 3.

=20You may generate small networks starting with a small number of authors = and papers and visualize a bipartite network or two unipartite networks. Th= e two input files have very specific formats so do not change the formats o= f the files provided. You can however change the input values in the iniscr= ipt.tarl file and generate networks with various numbers of authors and pap= ers. The agingfunction.txt can be changed by a distribution with a differen= t scale factor but the distribution should be properly normalized and shoul= d be in the same format, i.e. one column with the y values only.

=20The JAVA implementation is by Jeegar T. Maru and the algorithm has been = integrated by Ramya Sabbineni. Documentation was compiled by Soma Sanyal.=20

B=C3=B6rner, K., Maru, J. T. and Goldstone, R. L. (2004). The simultaneous evolution of author and paper networks.<= /a> PNAS. 101(Suppl_1):5266-5273.

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