graph-classification

Graph Classification

Recent years have witnessed an increasing number of applications involving objects with structural relationships, including chemical compounds in Bioinformatics, brain networks, image structures, and academic citation networks. For these applications, graph is a natural and powerful tool for modeling and capturing dependency relationships between objects. Unlike conventional data, where each instance is represented in a feature-value vector format, graphs exhibit node–edge structural relationships and have no natural vector representation. This challenge has motivated many graph classification algorithms in recent years.

Multiple structure-view learning for graph classification

Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph …

Boosting for graph classification with universum

Recent years have witnessed extensive studies of graph classification due to the rapid increase in applications involving structural data and complex relationships. To support graph classification, all existing methods require that training graphs …

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 designed for tasks with feature-vector represented instances, but cannot be applied to structure data, such as …

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 to target one single learning task and require a large number of labeled samples for learning good classification …

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 graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, …

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 from a set of labeled bags each containing a number of graphs inside the bag. A bag is labeled positive, if at least …

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 complex structure relationships. To date, all existing graph classification algorithms assume, explicitly or …

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 represent graphs for training classification models, extensive studies have been focused on extracting the most …

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 have useful information from different channels/views to describe objects, which naturally results in a new …