LIHAN: A Lattice-Guided Incomplete Heterogeneous Information Network Embedding Model for Node Classification

Guangxu Mei, Ziyu Guo, Li Pan, Qian Li, Feng Li, Shijun Liu

Abstract: Real-world heterogeneous information networks (HINs) are modeled as heterogeneous graphs, in which features and structures are often incomplete. Existing models employ manual imputation or dynamic adjustment to populate the incomplete data. However, there are some limitations in incomplete heterogeneous graph representation learning: 1) using populated data may lose content and high-level interaction information of HINs, even lead to negative impacts on the performance of downstream tasks; and 2) existing models fail to utilize the high-order heterogeneous structures in original incomplete network data. To resolve the above issues, in this article, we proposed a lattice-based incomplete heterogeneous structural attention network (LIHAN) for learning incomplete heterogeneous node embeddings. LIHAN first constructs characteristic lattice and structure lattice by mining characteristic sets and structure sets according to the partial order relations in between. Then, an improved lattice-based heterogeneous dual-attention mechanism is used to learn the heterogeneous node representations. Extensive node classification experiments are conducted on five open datasets to verify the superior performance of the proposed LIHAN model over the state-of-the-art models. Experimental results illustrate that LIHAN outperforms other methods on the micro-F1 and macro-F1 in node classification tasks. Moreover, experiments on different levels of lattices and the parameter sensitivity analysis shows the great stability during the process of experiments.

External IDs:dblp:journals/tcss/MeiGPLLL24