@techreport{oai:ipsj.ixsq.nii.ac.jp:00213832,
 author = {Jianming, Huang and Zhongxi, Fang and Hiroyuki, Kasai and Jianming, Huang and Zhongxi, Fang and Hiroyuki, Kasai},
 issue = {12},
 month = {Nov},
 note = {Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research because it has a wide range of applications such as bioinformatics, computer vision, social networks, and so on. However, the difficulty of graph classification is challenging and special, which is different from the ones of normal classification problems. One of the most difficult points of graph classification is that, the number of vertex neighbors in a graph tends to be variable, which makes the number of weights uncertain and ambiguous. In order to overcome these difficulties, we propose a novel method which weights the vertex neighbors based on a weighting function by learning the probability distributions of vertex pairs. The numerical evaluations show that our proposed method outperforms many state-of-the-art methods including some deep learning methods., Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research because it has a wide range of applications such as bioinformatics, computer vision, social networks, and so on. However, the difficulty of graph classification is challenging and special, which is different from the ones of normal classification problems. One of the most difficult points of graph classification is that, the number of vertex neighbors in a graph tends to be variable, which makes the number of weights uncertain and ambiguous. In order to overcome these difficulties, we propose a novel method which weights the vertex neighbors based on a weighting function by learning the probability distributions of vertex pairs. The numerical evaluations show that our proposed method outperforms many state-of-the-art methods including some deep learning methods.},
 title = {A Probability Distribution Learning Method for Extracting Key Vertex Pairs in Graph Classification Tasks},
 year = {2021}
}