Heterogeneous Graph Data Valuation: A Shapley Value-based Approach

With the widespread application of graph neural networks on heterogeneous graph data, accurately assessing the contribution of each node to model performance has become increasingly important. However, most existing graph data valuation methods are designed for homogeneous graphs and fail to fully account for the unique roles of different node types in heterogeneous graphs. This paper proposes a heterogeneous graph data valuation method based on an improved Shapley value, which effectively reduces computational complexity and enhances valuation accuracy by constructing local subgraphs and incorporating node-type information. Specifically, this paper first designs a subgraph construction strategy that considers node types, capturing local structural information by retaining key neighbor nodes. Second, it introduces an improved Shapley value calculation method, leveraging hierarchical sampling and type-aware importance evaluation to improve computational efficiency. Finally, a node-type-based validation mechanism is proposed, which verifies the valuation results by observing the impact of progressively removing nodes of different types on model performance. Experiments on the AIFB dataset demonstrate that the proposed method not only accurately identifies nodes significantly influencing node classification tasks but also reveals unique contribution patterns of different node types in heterogeneous graphs. The results highlight that publication-type nodes have the most significant impact on classification performance in academic knowledge graphs, providing critical guidance for the filtering and simplification of heterogeneous graph data.