2

Positive and unlabeled multi-graph learning
In this paper, we advance graph classification to handle multi-graph learning for complicated objects, where each object is represented …
DOI
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 …
DOI
Towards large-scale social networks with online diffusion provenance detection
In this paper we study a new problem of online discovering diffusion provenances in large networks. Existing work on network diffusion …
DOI
Classifying networked text data with positive and unlabeled examples
The rapid growth in the number of networked applications that naturally generate complex text data, which contains not only inner …
DOI
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 …
DOI
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 …
DOI
SODE: Self-adaptive one-dependence estimators for classification
SuperParent-One-Dependence Estimators (SPODEs) represent a family of semi-naive Bayesian classifiers which relax the attribute …
DOI
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 …
DOI
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 …
DOI
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 …
PDF Cite DOI