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This invention discloses a graph anonymization method for privacy protection in weighted social networks. It combines member fuzzy clustering and simulated annealing to create an optimal cluster of node degree sequences, resulting in a new degree sequence. Edge addition and deletion operations are performed on the original graph to reconstruct the graph and satisfy the new degree sequence. For nodes with the same degree, to resist background knowledge attacks, the edge weights of some nodes are generalized so that the weight values ​​of nodes with the same degree satisfy a diversity model. Experimental results show that, compared with other methods, the combination of member fuzzy clustering and simulated annealing provided by this invention can not only resist background knowledge attacks on node degree and weighted edges in weighted social networks, but also effectively reduce the amount of data loss after anonymization and improve the actual utility of the data.