局部相似性指标因计算开销低而被广泛应用于复杂网络链路预测；然而，在稀疏网络中，它们对所有缺乏共同邻居的节点对赋予零分，严重限制了其预测能力。一种自然的改进方案是将节点中心性指标与局部相似性指标相融合：前者刻画节点对的全局重要性，后者捕捉细粒度的局部拓扑结构，二者可在统一框架下生成互补的评分。本文选取PageRank和DomiRank作为两类代表性中心性度量，构建了中心性–局部相似性融合框架。首先，将Charikhi提出的基于PageRank的融合方法推广至七种经典局部相似性指标，并在九个真实世界网络数据集上系统验证了该改进的普适性。进一步，引入DomiRank中心性，在统一权重系数下构建DR-MD系列融合指标，克服了基于PageRank的融合需对每个指标单独调权的缺陷。五折交叉验证及Wilcoxon符号秩检验结果表明，在统一实验协议下，所有DR-MD指标在全部九个数据集上均持续优于对应局部基线方法及其PR-MD counterparts（p=0.002）；且该提升在近临界参数平台内对σ及权重系数的扰动具有鲁棒性；其中DR-RA平均AUC达0.7084，超越Katz、RWR等全局方法及若干先进相似性指标。该框架具有天然可扩展性，其融合范式可直接推广至

Local similarity indices are widely used in link prediction on complex networks owing to their low computational cost; however, in sparse networks they assign a zero score to every node pair lacking common neighbors, which severely limits their predictive power. A natural remedy is to fuse node centrality indices with local similarity indices: the former provide global importance for the node pair, while the latter capture fine-grained local topology, and the two can be combined into complementary scores within a unified framework. This paper uses PageRank and DomiRank as two representative centrality measures and constructs a centrality--local-similarity fusion framework. The PageRank-based fusion proposed by Charikhi is first generalized to seven classical local similarity indices, and the universality of its improvement is systematically verified on nine real-world network datasets. Furthermore, the DomiRank centrality is introduced to build the DR-MD series of fused indices under a unified weighting coefficient, which overcomes the drawback that the PageRank-based fusion requires index-by-index weight tuning. Results of five-fold cross-validation together with Wilcoxon signed-rank tests show that, under the unified experimental protocol, all DR-MD indices consistently outperform the corresponding local baselines and their PR-MD counterparts on all nine datasets ($p=0.002$), and that the improvements remain robust against perturbations of $σ$ and the weighting coefficients within the near-critical parameter plateau; in particular, DR-RA achieves an average AUC of 0.7084, surpassing global methods such as Katz and RWR as well as several advanced similarity indices. The framework is inherently extensible, and its fusion paradigm can be straightforwardly generalized to couple other node centrality indices with local similarity indices.