Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate ...Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.展开更多
Modeling and analysis of complex social networks is an important topic in social computing.Graph convolutional networks(GCNs)are widely used for learning social network embeddings and social network analysis.However,r...Modeling and analysis of complex social networks is an important topic in social computing.Graph convolutional networks(GCNs)are widely used for learning social network embeddings and social network analysis.However,real-world complex social networks,such as Facebook and Math,exhibit significant global structural and dynamic characteristics that are not adequately captured by conventional GCN models.To address the above issues,this paper proposes a novel graph convolutional network considering global structural features and global temporal dependencies(GSTGCN).Specifically,we innovatively design a graph coarsening strategy based on the importance of social membership to construct a dynamic diffusion process of graphs.This dynamic diffusion process can be viewed as using higher-order subgraph embeddings to guide the generation of lower-order subgraph embeddings,and we model this process using gate recurrent unit(GRU)to extract comprehensive global structural features of the graph and the evolutionary processes embedded among subgraphs.Furthermore,we design a new evolutionary strategy that incorporates a temporal self-attention mechanism to enhance the extraction of global temporal dependencies of dynamic networks by GRU.GSTGCN outperforms current state-of-the-art network embedding methods in important social networks tasks such as link prediction and financial fraud identification.展开更多
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
基金supported by the Natural Science Foundation of China(No.U22A2099)the Innovation Project of Guangxi Graduate Education(YCBZ2023130).
文摘Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.
基金supported by the National Natural Science Foundation of China(Nos.62002063 and U21A20472)Natural Science Foundation of Fujian Province(Nos.2020J05112 and 2022J01118)+1 种基金National Key Research and Development Plan of China(No.2021YFB3600503)Major Science and Technology Project of Fujian Province(No.2021HZ022007).
文摘Modeling and analysis of complex social networks is an important topic in social computing.Graph convolutional networks(GCNs)are widely used for learning social network embeddings and social network analysis.However,real-world complex social networks,such as Facebook and Math,exhibit significant global structural and dynamic characteristics that are not adequately captured by conventional GCN models.To address the above issues,this paper proposes a novel graph convolutional network considering global structural features and global temporal dependencies(GSTGCN).Specifically,we innovatively design a graph coarsening strategy based on the importance of social membership to construct a dynamic diffusion process of graphs.This dynamic diffusion process can be viewed as using higher-order subgraph embeddings to guide the generation of lower-order subgraph embeddings,and we model this process using gate recurrent unit(GRU)to extract comprehensive global structural features of the graph and the evolutionary processes embedded among subgraphs.Furthermore,we design a new evolutionary strategy that incorporates a temporal self-attention mechanism to enhance the extraction of global temporal dependencies of dynamic networks by GRU.GSTGCN outperforms current state-of-the-art network embedding methods in important social networks tasks such as link prediction and financial fraud identification.
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.
