GIG: Graph Data Imputation With Graph Differential Dependencies

TL;DR

GIG introduces a graph data imputation method leveraging graph differential dependencies and transformer models, enhancing reliability, explainability, and semantic integration in missing data prediction within graph datasets.

Contribution

The paper proposes GIG, a novel graph data imputation approach that learns graph differential dependencies and employs transformers, addressing limitations of existing methods in generalization and explainability.

Findings

GIG outperforms existing methods on seven real-world datasets.

GIG effectively incorporates semantic knowledge through GDDs.

The approach enhances explainability in graph data imputation.

Abstract

Data imputation addresses the challenge of imputing missing values in database instances, ensuring consistency with the overall semantics of the dataset. Although several heuristics which rely on statistical methods, and ad-hoc rules have been proposed. These do not generalise well and often lack data context. Consequently, they also lack explainability. The existing techniques also mostly focus on the relational data context making them unsuitable for wider application contexts such as in graph data. In this paper, we propose a graph data imputation approach called GIG which relies on graph differential dependencies (GDDs). GIG, learns the GDDs from a given knowledge graph, and uses these rules to train a transformer model which then predicts the value of missing data within the graph. By leveraging GDDs, GIG incoporates semantic knowledge into the data imputation process making it more reliable and explainable. Experimental results on seven real-world datasets highlight GIG's effectiveness compared to existing state-of-the-art approaches.