Big graph classification frameworks based on Extreme Learning Machine

Abstract

Graph data analysis is a hot topic in recent research area. Graph classification is one of the most important graph data analysis problems, which choose the most probable class labels of graphs using models based on the training dataset. It has wildly applications in protein group identification, chemical compounds classification and so on. Many existing research of graph learning suffer from high computation cost as large scale graph data are dramatically increased. In order to realize big graph classification with real-time learning ability and good scalability, efficient feature extraction approaches and ELM variants are utilized in this paper. To be specific, we present three frameworks of big graph classification based on ELMs: (1) a framework with a compression-based frequent subgraph mining method to reduce graph size; (2) an incremental framework to handle dynamic graphs; (3) a distributed framework with distributed ELMs to provide good scalability and easy implementation on cloud platforms. Extensive experiments are conducted on clusters with large real-world graph datasets. The experimental results demonstrate that our frameworks are efficient in big graph classification applications, and well suitable for dynamic networks. The results also validate that ELM and its variants have good classification performance on large-scale graphs.