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Factor Graph Models

     Factor graph is one type of probabilistic graphical models, providing an elegant way to represent both undirected graphical structure and directed graphical structure, with more emphasis on the factorization of the distribution.
    Online social networks are getting larger and machine learning tasks are facing several challenges:

    (1) labeled data is insufficient and how to leverage the unlabeled data for learning a graphical model?

    (2) how to leverage the correlation and the network information to help build the graphical model?
    We design two categories of factor graph models. The first category is for unsupervised learning. We have proposed Topical Factor Graph (TFG) (Tang et al., KDD'09), Time-constrained Probabilistic Factor Graph model (TPFG) (Wang et al., KDD'10). The second category is for supervised learning. We have proposed Partially Labeled Factor Graph model (PLFG) (Zhuang et al., DMKD'12), Triad-based Factor Graph model (TriFG) (Lou et al., TKDD'12), Transfer-based Factor Graph model (TranFG) (Tang et al., WSDM'12).

Related codes:

[Partially Labeled Factor Graph  readme. Refer to Web page or Tang et al. PKDD'11 for details.]
[Topic Affinity Propagation. Refer to Web Page or Tang et al. KDD'09 for details.]

创建: Apr 11, 2018 | 20:41