Toward graph classification on structure property using adaptive motif based on graph convolutional network
Toward graph classification on structure property using adaptive motif based on graph convolutional network
复制标题
基于图卷积网络的自适应模体结构属性图分类
DOI：
10.1007/s11227-021-03628-4
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Hongxi Wu
中科院分区：
文献类型：
--
作者：
Xingquan Li;Hongxi Wu
图形卷积网络（GCN）已被广泛用于处理各种图形数据分析任务。对于图形分类任务，图形分类的现有工作主要关注图相似性的两个方面：物理结构和实用性
Graph convolutional network (GCN) has been widely used in handling various graph data analysis tasks. For graph classification tasks, existing work on graph classification mainly focuses on two aspects of graph similarity: physical structure and practical property. In this paper, we consider the problem of graph classification from a new perspective, namely structural properties. Graph similarity is defined based on structural properties such as maximum clique, minimum vertex coverage, and minimum dominating set of graphs. To capture these structural features, we design an adaptive motif to mine the higher-order connectivity information among nodes. Furthermore, to obtain the unique down-sampling in graph pooling stage, we propose a de-correlation pooling approach. Our extensive experiments on several artificially generated datasets show that our proposed model can effectively classify graphs with similar structural property. It is also experimentally compared with the baseline approach to demonstrate the effectiveness of our adaptive motif GCNs.
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DOI：
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