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- W2376769549 abstract "Nowadays,the graph classification has become an important and active research topic in the last decade,which has a wild variety of real world applications.Current research on graph classification assumes that train and test data are drawn from the same feature space and same distribution.However,in many applications,the train graph data and test data are not dawn from the same distribution.In this paper,we study the transfer learning for graph classification and propose a novel solution,TraGboost,to efficiently build a graph classification model with a few labeled graph data and amounts of relation graph data.TrGboost,which based of boosting,allows users to utilize amounts of relation labeled graph data to construct a classification model for the target graph data.Empirical studies on real-world tasks demonstrate that our method can effectively boost graph classification with transfer learning." @default.
- W2376769549 created "2016-06-24" @default.
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- W2376769549 date "2011-01-01" @default.
- W2376769549 modified "2023-09-22" @default.
- W2376769549 title "Transfer Learning for Graph Classification" @default.
- W2376769549 hasPublicationYear "2011" @default.
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