英文论文
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文献类型
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Article
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题名
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Community influence analysis in social networks
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作者
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Chen, Yuanxing; Fang, Kuangnan; Lan, Wei; Tsai, Chih-Ling; Zhang, Qingzhao
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作者单位
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[Chen, Yuanxing] Tsinghua Univ, Yau Math Sci Ctr, Beijing, Peoples R China. [Fang, Kuangnan; Zhang, Qingzhao] Xiamen Univ, Dept Stat & Data Sci, Xiamen, Peoples R China. [Lan, Wei] Southwestern Univ Finance & Econ, Sch Stat, Chengdu, Peoples R China. [Lan, Wei] Southwestern Univ Finance & Econ, Ctr Stat Res, Chengdu, Peoples R China. [Tsai, Chih-Ling] Univ Calif Davis, Grad Sch Management, Davis, CA USA. [Zhang, Qingzhao] Xiamen Univ, Wang Yanan Inst Studies Econ, Xiamen, Peoples R China.
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通讯作者地址
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Xiamen Univ, Dept Stat & Data Sci, Xiamen, Peoples R China.; Southwestern Univ Finance & Econ, Sch Stat, Chengdu, Peoples R China.; Southwestern Univ Finance & Econ, Ctr Stat Res, Chengdu, Peoples R China.
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Email
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lanwei@swufe.edu.cn; qzzhang@xmu.edu.cn
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ResearchID
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ORCID
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期刊名称
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COMPUTATIONAL STATISTICS & DATA ANALYSIS
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出版社
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ELSEVIER
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ISSN
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0167-9473
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出版信息
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2025-02, 202
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JCR
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影响因子
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ISBN
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基金
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会议名称
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会议地点
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会议开始日期
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会议结束日期
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关键词
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Fused lasso; Influence power; Nodal heterogeneity; Quasi-maximum likelihood estimator; Subgroup analysis
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摘要
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Heterogeneous influence detection across network nodes is an important task in network analysis. A community influence model (CIM) is proposed to allow nodes to be classified into different communities (i.e., clusters or groups) such that the nodes within the same community share the common influence parameter. Employing the quasi-maximum likelihood approach, together with the fused lasso-type penalty, both the number of communities and the influence parameters can be estimated without imposing any specific distribution assumption on the error terms. The resulting estimators are shown to enjoy the oracle property; namely, they perform as well as if the true underlying network structure were known in advance. The proposed approach is also applicable for identifying influential nodes in a homogeneous setting. The performance of our method is illustrated via simulation studies and two empirical examples using stock data and coauthor citation data, respectively.
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一级学科
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Computer Science, Interdisciplinary Applications; Statistics & Probability
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WOS入藏号
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WOS:001306282300001
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EI收录号
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20243616991970
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DOI
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10.1016/j.csda.2024.108037
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ESI
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收录于
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SCIE, EI
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