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This paper proposes PreGS, a parameter-transfer-based multi-expert graph neural network framework, and develops PreGSv2, which introduces source-level weighting and a structural gating mechanism for adaptive multi-source feature integration.
SimGAT, a structure-aware graph attention model built on SimRank-derived structural embeddings, is proposed, which computes structural similarity in the SimRank2Vec embedding space and injects it as a topological prior into the graph attention mechanism, enabling neighborhood aggregation to be jointly guided by node at...
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