ResLPA: Label Propagation With Residual Learning

Yi Luo, Rui Huang, Aiguo Chen, Xi Zeng

Abstract: Label Propagation Algorithm (LPA) predicts what label a node in a graph has by its accumulated label information after iteratively propagating label information among adjacent nodes. Subsequent studies of LPA try to assign weights on connections to adjust the propagation between different node pairs. However, such approaches still rely on the assumption that adjacent nodes should have similar labels. To predict when the label distribution may differ from this assumption, we propose Residual Label Propagation (ResLPA). It approximates residuals between labels with node features, then adds the approximated residuals to the label information as corrections before propagating. In experiments on five real-world graphs, ResLPA matches or exceeds state-of-the-art LPA-based methods, GCN-based methods and their integrations.