Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks

著者: Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu C. Aggarwal, Prasenjit Mitra, Suhang Wang - ACM International Conference on Information and Knowledge Management, CIKM 2020 被引用: 94

Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkable success of GCNs, it is difficult to train GCNs with insufficient supervision. When labeled data are limited, the performance of GCNs becomes unsatisfying for low-degree nodes. While some prior work analyze successes and failures of GCNs on the entire model level, profiling GCNs on individual node level is still underexplored.