Liao Hao, Xiao Rui, Cimini Giulio, Medo Matúš
Physics Department, University of Fribourg, Fribourg, Switzerland.
Physics Department, University of Fribourg, Fribourg, Switzerland; Institute for Complex Systems (ISC-CNR) and Department of Physics, "Sapienza" University of Rome, Rome, Italy.
PLoS One. 2014 Dec 2;9(12):e112022. doi: 10.1371/journal.pone.0112022. eCollection 2014.
The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.
科学产出数量的不断增加和复杂性的日益提高，使得研究人员难以追踪其所在领域的进展。这一点，再加上在线科学社区越来越受欢迎，都需要开发有效的信息过滤工具。我们在此提出一种算法，该算法能在表示在线科学社区的二分网络中同时计算用户的声誉和论文的适配度。对人工生成的数据以及来自经济物理学论坛的真实数据进行评估，以确定该方法表现最佳的变体。我们表明，当输入数据扩展到包括用户、论文和作者的多层网络，并相应修改算法时，最终性能会在多个层面上得到提升。特别是，顶级论文的被引次数更高，顶级作者的h指数比其他算法选出的顶级论文和顶级作者更高。我们最终表明，我们的算法对长期存在的作者（垃圾信息发送者）具有鲁棒性，这使得该方法能够很容易地应用于现有的在线科学社区。
