Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, Suhang Wang
Abstract
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.
Topics & Concepts
Computer scienceProfiling (computer programming)GraphMachine learningArtificial intelligenceDeep learningData miningData scienceNode (physics)Training setConvolutional neural networkNoisy dataGraph theoryData modelingData collectionWork (physics)Advanced Graph Neural NetworksComplex Network Analysis TechniquesGraph Theory and Algorithms