Abstract
A multi-graph is represented by a bag of graphs and modeled as a generalization of a multi-instance. Multi-graph classification is a supervised learning problem, which has a wide range of applications, such as scientific publication categorization, bio-pharmaceutical activity tests and online product recommendation. However, existing algorithms are limited to process small datasets due to high computation complexity of multi-graph classification. Specially, the precision is not high enough for a large dataset. In this paper, we propose a scalable and high-precision parallel algorithm to handle the multi-graph classification problem on massive datasets using MapReduce and extreme learning machine. Extensive experiments on real-world and synthetic graph datasets show that the proposed algorithm is effective and efficient.
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