In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is ...In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos. 11131002, 11271031, 71532001, 11525101, 71271210 and 714711730)the Business Intelligence Research Center at Peking University+5 种基金the Center for Statistical Science at Peking Universitythe Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China (Grant No. 16XNLF01)Ministry of Education Humanities Social Science Key Research Institute in University Foundation (Grant No. 14JJD910002)the Center for Applied Statistics, School of Statistics, Renmin University of ChinallChina Postdoctoral Science Foundation (Grant No. 2016M600155)
文摘In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.
