Multi-graph-view Learning for Graph Classification

Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang
2014 2014 IEEE International Conference on Data Mining  
In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), which aims to
more » ... re subgraph features from multiple graphviews for learning. By enabling a joint regularization across multiple graph-views, and enforcing labeling constraints at the bag and graph levels, MGVBL is able to discover most effective subgraph features across all graph-views for learning. Experiments on real-world learning tasks demonstrate the performance of MGVBL for complicated object classification.
doi:10.1109/icdm.2014.97 dblp:conf/icdm/WuHPZCZ14 fatcat:2ax3yvc4mjg3nhw4uh6kwuj63i