Uncertain Graph Classification Based on Extreme Learning Machine

Abstract

The problem of graph classification has attracted much attention in recent years. The existing work on graph classification has only dealt with precise and deterministic graph objects. However, the linkages between nodes in many real-world applications are inherently uncertain. In this paper, we focus on classification of graph objects with uncertainty. The method we propose can be divided into three steps: Firstly, we put forward a framework for classifying uncertain graph objects. Secondly, we extend the traditional algorithm used in the process of extracting frequent subgraphs to handle uncertain graph data. Thirdly, based on Extreme Learning Machine (ELM) with fast learning speed, a classifier is constructed. Extensive experiments on uncertain graph objects show that our method can produce better efficiency and effectiveness compared with other methods.