Graph Classification

Multiple structure-view learning for graph classification
Many applications involve objects containing structure and rich content information, each describing different feature aspects of the …
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Boosting for graph classification with universum
Recent years have witnessed extensive studies of graph classification due to the rapid increase in applications involving structural …
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Task sensitive feature exploration and learning for multitask graph classification
Multitask learning (MTL) is commonly used for jointly optimizing multiple learning tasks. To date, all existing MTL methods have been …
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Joint structure feature exploration and regularization for multi-task graph classification
Graph classification aims to learn models to classify structure data. To date, all existing graph classification methods are designed …
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Multi-graph-view subgraph mining for graph classification
In this paper, we formulate a new multi-graph-view learning task, where each object to be classified contains graphs from multiple …
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Boosting for multi-graph classification
In this paper, we formulate a novel graph-based learning problem, multi-graph classification (MGC), which aims to learn a classifier …
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CogBoost: boosting for fast cost-sensitive graph classification
Graph classification has drawn great interests in recent years due to the increasing number of applications involving objects with …
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Finding the best not the most: regularized loss minimization subgraph selection for graph classification
Classification on structure data, such as graphs, has drawn wide interest in recent years. Due to the lack of explicit features to …
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Multi-graph-view Learning for Graph Classification
Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to …
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