A multi-round iterative network alignment method is proposed to address the challenges of large structural differences and high noise sensitivity in anchor nodes in network alignment tasks.The method calculates node features of different dimensions using various heuristic approaches at each iteration,utilizing the combination of multiple features to assess the reliability of anchor nodes,filter potential noise,and enhance the confidence of each alignment round.Additionally,a graph neural network is employed to im-prove the consistency between nodes without attributes,mitigating the impact of structural differences in networks.Experimental re-sults demonstrate that this method achieves high accuracy under high noise conditions,verifying its effectiveness.
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