Weighted Graph Classification by Self-Aligned Graph Convolutional Networks Using Self-Generated Structural Features
Directed weighted graphs are important graph data. The weights and directions of the edges carry rich information which can be utilized in many areas. For instance, in a cashflow network, the direction and amount of a transfer can be used to detect social ties or criminal organizations. Hence it is important to study the weighted graph classification problems. In this paper, we present a graph classification algorithm called Self-Aligned graph convolutional network (SA-GCN) for weighted graph classification. SA-GCN first normalizes a given graph so that graphs are trimmed and aligned in correspondence. Following that structural features are extracted from the edge weights and graph structures. And finally the model is trained in an adversarial way to make the model more robust. Experiments on benchmark datasets showed that the proposed model could achieve competitive results and outperformed some popular state-of-the-art graph classification methods.
Graph classification Graph convolutional networks Graph normalization Structural features Adversarial training
Xuefei Zheng Min Zhang Jiawei Hu Weifu Chen Guocan Feng
School of Mathematics,Sun Yat-sen University,Guangzhou,China;Tencent-CDG-FIT,Shenzhen,China School of Mathematics,Sun Yat-sen University,Guangzhou,China Tencent-CDG-FIT,Shenzhen,China
国际会议
广州
英文
505-516
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）