Social network structures can crucially impact complex social processes such as collective behaviour or the transmission of information and diseases. However, currently it is poorly understood how social networks chan...Social network structures can crucially impact complex social processes such as collective behaviour or the transmission of information and diseases. However, currently it is poorly understood how social networks change over time. Previous studies on primates suggest that ＇knockouts＇ （due to death or dispersal） of high-ranking individuals might be important drivers for structural changes in animal social networks. Here we test this hypothesis using long-term data on a natural population of ba- boons, examining the effects of 29 natural knockouts of alpha or beta males on adult female social networks. We investigated whether and how knockouts affected （i） changes in grooming and association rates among adult females, and （2） changes in mean degree and global clustering coefficient in these networks. The only significant effect that we found was a decrease in mean degree in grooming networks in the first month after knockouts, but this decrease was rather small, and grooming networks re- bounded to baseline levels by the second month after knockouts. Taken together our results indicate that the removal of high-ranking males has only limited or no lasting effects on social networks of adult female baboons. This finding calls into question the hypothesis that the removal of high-ranking individuals has a destabilizing effect on social network structures in social animals [Current Zoology 61 （1）： 107-113, 2015].展开更多
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
文摘Social network structures can crucially impact complex social processes such as collective behaviour or the transmission of information and diseases. However, currently it is poorly understood how social networks change over time. Previous studies on primates suggest that ＇knockouts＇ （due to death or dispersal） of high-ranking individuals might be important drivers for structural changes in animal social networks. Here we test this hypothesis using long-term data on a natural population of ba- boons, examining the effects of 29 natural knockouts of alpha or beta males on adult female social networks. We investigated whether and how knockouts affected （i） changes in grooming and association rates among adult females, and （2） changes in mean degree and global clustering coefficient in these networks. The only significant effect that we found was a decrease in mean degree in grooming networks in the first month after knockouts, but this decrease was rather small, and grooming networks re- bounded to baseline levels by the second month after knockouts. Taken together our results indicate that the removal of high-ranking males has only limited or no lasting effects on social networks of adult female baboons. This finding calls into question the hypothesis that the removal of high-ranking individuals has a destabilizing effect on social network structures in social animals [Current Zoology 61 （1）： 107-113, 2015].
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.
