Robust Neural Model for Searching over Incomplete Graphs

The task of searching over large keyword graphs aims to identify a subgraph where the nodes collectively cover the input query keywords. Although finding an exact solution to this problem is NP-hard, we address it by proposing a novel graph neural network representation learning technique specifically tailored for graphs with missing information. We propose a novel keyword graph representation learning method that incorporates complementary aspects of graphs: global, local, adjusted, and feature semantics. Considering these multiple aspects, our approach remains robust and resilient to missing information. We adopt and fine-tune a transformer-based model to aggregate the various features of a graph to generate rich representations, recognizing the pivotal role of keywords in this task. We show through experiments on real-world data that our method outperforms the state-of-the-art approaches and is particularly robust in the face of missing values, underscoring its ability to effectively handle incomplete graphs.