Structure learning for weighted networks based on Bayesian nonparametric models
Jiang XJ(蒋晓娟)1; Zhang WS(张文生)1; zhang wensheng
2016-06
发表期刊INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
卷号7期号:3页码:479-489
摘要With the increase of availability and scope of complex networks, structure learning for networks has received an enormous amount of interest in many fields, including physics, computer and information sciences, biology and the social sciences. To extract compact and flexible representations for weightednetworks, we propose a new Bayesian nonparametric model to learn from both the existence and weight of interactions between nodes. Our model adopts Dirichlet process prior to automatically infer the partition over nodes in weighted networks without specifying the number of clusters. This is vital for structurediscovery in complex networks, especially for novel domains where we have little prior knowledge. We develop a mean-field variational algorithm to efficiently approximate the model's posterior distribution over infinite latent clusters. Conducting extensive experiments on synthetic data set and four popular data sets, we demonstrate that our model can effectively capture the latent structure for complex weighted networks.
关键词Structure Learning Clustering Probabilistic Graph Models Bayesian Nonparametric Models Variational Inference
学科领域Computer Science, Artificial Intelligence
DOI10.1007/s13042-015-0439-1
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文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20350
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者zhang wensheng
作者单位1.中国科学院自动化研究所
2.中国科学院自动化研究所
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Jiang XJ,Zhang WS,zhang wensheng. Structure learning for weighted networks based on Bayesian nonparametric models[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2016,7(3):479-489.
APA Jiang XJ,Zhang WS,&zhang wensheng.(2016).Structure learning for weighted networks based on Bayesian nonparametric models.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,7(3),479-489.
MLA Jiang XJ,et al."Structure learning for weighted networks based on Bayesian nonparametric models".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 7.3(2016):479-489.
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