基于多级联合的图池化方法

Graph pooling method based on multilevel union

图池化方法已经在生物信息学、化学、社交网络、推荐系统等多个领域中得到广泛应用,但关于图池化方法大多没有很好的解决节点选择问题和池化带来的节点信息丢失问题。对此提出一种新的多级联合图池化(MUPool)方法。所提方法使用多视角模块从多个视角获取节点的特征,即通过多个卷积模块提取不同的特征。同时提出多级联合模块(级联),将不同池化层的输出串联,每一层都可以融合以往所有层的信息。提出使用后端融合模块,针对每个池化层建立一个分类器,对预测结果进行融合得到最终分类结果。所提方法在多个数据集上进行实验,准确度平均提高1.62%,所提方法可以与现有的分层池化方法相结合,结合后的方法准确度平均提高2.45%。

Graph pooling method has been widely used in bioinformatics,chemistry,social networks,recommendation systems and other fields.At present,the graph pooling method does not solve the problem of node selection and node information loss caused by pooling.A new graph pooling method is proposed,namely the graph pooling method based on multilevel union(MUPool).The suggested technique extracts distinct features from several convolution modules by using a multi-view module to obtain the properties of nodes from various angles.At the same time,a multilevel union module is proposed to concatenate the outputs of different pooling layers,each layer fusing information from all previous layers.The suggested approach builds a classifier based on each pooling layer using the late fusion module,then fuses the predicted results to obtain the final classification results.The proposed method is tested on multiple data sets,and the accuracy is improved by 1.62%on average,the proposed method can be combined with the existing hierarchical pooling method,the accuracy of the combined method is improved by 2.45%on average.