Hierarchical Label Inference Incorporating Attribute Semantics in Attributed Networks

Junliang Li, Yajun Yang, Qinghua Hu, Xin Wang, Hong Gao

Abstract: Node attribute label inference is an important problem in attributed networks. Most existing works assume that node labels are at a single level, but in practice, the attribute labels can always be organized in a hierarchical structure according to their semantics. In this paper, we propose a novel hierarchical label inference model for attributed networks. Specifically, we propose a triple attention mechanism to extract fine-grained label semantics from three levels: hierarchical, sibling and global. Next, we propose the semantic fully-connected layer to explicitly exploit label semantics for attribute inference. We also propose semantic label propagation to enhance the interaction between the label semantics and the attributed network, and this interaction enables nodes in the attributed network to realise the proximity assumption at the label semantic level. Finally, we combine the semantic fully-connected layer with semantic label propagation for top-down hierarchical attribute inference. Extensive experiments demonstrate the superiority of our model.