Building Robust and Trustworthy HGNN Models: A Learnable Threshold Approach for Node Classification

Message passing scheme is a general idea for Graph Neural Networks (GNNs) to learn node representations. During message passing, given a target node, we transform and aggregate the feature vectors of its neighbors and generate a representation vector for the target node. However, real-world graph data is usually constructed from complicated scenarios based on manually pre-defined rules; it is often the case that noisy information gets involved in message passing, thereby resulting in sub-optimal performance for GNNs and also impacting their trustworthiness and reliability. In this study, we present an effective learnable threshold technique that explicitly optimizes heterogeneous graph structure with the goal to maximize performance improvement of GNNs for downstream tasks. We give an explanation about the design of the learnable threshold and show the ability that our model can be applied to large-scale graphs. Experiments on seven datasets show that our model has a powerful ability to deal with homogeneous graphs with low homophily ratio and dense graphs. With the verification of robustness analysis, our model can resist the noisy information, which proves the robustness of our model.