Graph Classification Based on Sparse Graph Feature Selection and Extreme Learning Machine

Abstract

Identification and classification of graph data is a hot research issue in pattern recognition. The conventional methods of graph classification usually convert the graph data to vector representation which ignore the sparsity of graph data. In this paper, we propose a new graph classification algorithm called graph classification based on sparse graph feature selection and extreme learning machine. The key of our method is using lasso to select sparse feature because of the sparsity of the corresponding feature space of the graph data, and extreme learning machine (ELM) is introduced to the following classification task due to its good performance. Extensive experimental results on a series of benchmark graph datasets validate the effectiveness of the proposed methods.