Modeling And Predicting Scientific Literature's Citation Via Temporal Point Process

Categories of scientific literature include journal,conference paper,patent,techni-cal report,dissertation,etc.The literature is turning into an unbounded collection as the rapid development of science and technology,it is of great importance to evaluate the cur-rent and potential impact as accurately as possible in fields such as personnel recruitment,funding,patent infringement lawsuit.It is widely believed that the value of literature is often closely related to peer-citations,which makes research on citation behavior an im-portant topic.On the basis of specific insight on literature citation data,this work attempts to model the peer-citation behavior,proposing a multi-dimensional temporal point process model;Based on maximum likelihood evaluation(MLE)and EM method,we have de-signed and implemented a highly efficient evaluation algorithm to train this model rapidly;furthermore,we have applied discriminative and adversarial learning method to devise a full training procedure,in order to further enhance prediction ability of point process;we conduct experiments on real citation data of academic paper data(originating from Microsoft Academic Graph[1])and patent data(originating from NBER[2]);besides,we have also discussed the implementation of an academic search and prediction engine.The proposed model is capable of evaluating future citations in an arbitrary period,thus en-abling the predictive analysis for literature's impact.This work designs an effective tool for quantifying long-term scientific and technical impact,offering suggestions for liter-ature' s potential value,promoting the application of machine learning method in this field,and is an instance that AI technology plays an importance role in real life problems.Meanwhile,proposed model can also be applied in fields such as high frequency trading,merger and acquisition,career path modeling,etc.