Generalized Taxonomy-Guided Graph Neural Networks
Generalized Taxonomy-Guided Graph Neural Networks
Yu Zhou, Di Jin, Jianguo Wei, Dongxiao He, Zhizhi Yu, Weixiong Zhang
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 2616-2624.
https://doi.org/10.24963/ijcai.2024/289
Graph neural networks have been demonstrated to be effective analytic apparatus for mining network data. Most real-world networks are inherently hierarchical, offering unique opportunities to acquire latent, intrinsic network organizational properties by utilizing network taxonomies. The existing approaches for learning implicit hierarchical network structures focus on introducing taxonomy to graph neural networks but often run short of exploiting the rich network semantics and structural properties in the taxonomy, resulting in poor generalizability and reusability. To address these issues, we propose generalized Taxonomy-Guided Graph Neural Networks (TG-GNN) to integrate taxonomy into network representation learning. We first construct a taxonomy representation learning module that introduces the concept of ego network to propagate and aggregate rich semantic and structural information in the taxonomy. We then design a taxonomy-guided Markov mechanism, which encapsulates taxonomy knowledge in pairwise potential functions, to refine network embeddings. Extensive experiments on various real-world networks illustrate the effectiveness of TG-GNN over the state-of-the-art methods on scenarios involving incomplete taxonomies and inductive settings.
Keywords:
Data Mining: DM: Mining graphs
Machine Learning: ML: Sequence and graph learning