Network alignment is a problem of finding the node mapping between similar networks. It links the data from separate sources and is widely studied in bioinformation and social network fields. The critical difference between network alignment and exact graph matching is that the network alignment considers node mapping in non-isomorphic graphs with error tolerance. Researchers usually utilize AC (accuracy) to measure the performance of network alignments which comparing each output element with the benchmark directly. However, this metric neglects that some nodes are naturally indistinguishable even in single graphs (e.g., nodes have the same neighbors) and no need to distinguish across graphs. Such neglect leads to the underestimation of models. We propose an unbiased metric for network alignment that takes indistinguishable nodes into consideration to address this problem. Our detailed experiments with different scales on both synthetic and real-world datasets demonstrate that the proposed metric correctly reflects the deviation of result mapping from benchmark mapping as standard metric AC does. Comparing with the AC, the proposed metric effectively blocks the effect of indistinguishable nodes and retains stability under increasing indistinguishable nodes.

中文翻译：

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SNAC：一种评估拓扑识别网络对齐能力的无偏度量

网络对齐是寻找相似网络之间的节点映射的问题。它链接来自不同来源的数据，并在生物信息和社交网络领域得到广泛研究。网络对齐和精确图匹配之间的关键区别在于网络对齐考虑了具有容错性的非同构图中的节点映射。研究人员通常使用 AC（准确度）来衡量网络对齐的性能，将每个输出元素与基准直接进行比较。然而，这个度量忽略了一些节点即使在单个图中也自然无法区分（例如，节点具有相同的邻居）并且不需要跨图区分。这种忽视会导致对模型的低估。我们提出了一个无偏的网络对齐度量，它考虑了不可区分的节点来解决这个问题。我们在合成数据集和真实数据集上进行的不同尺度的详细实验表明，所提出的度量标准正确地反映了结果映射与基准映射的偏差，就像标准度量标准 AC 所做的那样。与 AC 相比，所提出的度量有效地阻止了不可区分节点的影响，并在增加不可区分节点的情况下保持稳定性。