<ul><li>Real-world graph data environments often contain noise that affects the effectiveness of graph representation and downstream learning tasks.</li><li>Existing methods for homogeneous graphs synthesize a similarity graph based on original node features to correct the structure of the noisy graph.</li><li>However, similar nodes in heterogeneous graphs do not have direct links, posing a challenge for noise correction in heterogeneous graph learning.</li><li>This paper proposes a novel synthesized similarity-based graph neural network for learning from noisy heterogeneous graphs, achieving state-of-the-art results in various real-world datasets.</li></ul>

NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning

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