Network alignment (NA) aims to link common nodes across multiple networks and is an essential task in many graph mining applications. Despite the progress achieved by many recent works, several fundamental limitations have eluded the proper cohesive way of addressing, including matching confusion, lack of the formal treatment of uncertainty, and Point-to-Point (P2P) constraint. This study proposes a novel framework UANA (Uncertainty-Aware Network Alignment) to tackle the limitations of the existing works. By embedding nodes as Gaussian distributions rather than point vectors, UANA enables to capture the uncertainty of a node representation, while being able to discriminate the anchor nodes from the potentially confusing neighbors. We address the P2P matching constraint by introducing an adversarial learning paradigm, which relaxes the exact matching assumption during training with an across-domain generative procedure to reduce the matching errors on testing nodes. In the end, interpretability methods are included to explain the aligning results made by our UANA based on the robust statistics, which enables the explanation of the effect of individual training sample on the NA performance without the need of retraining the model. Extensive experiments conducted on real-world data sets demonstrate that UANA significantly outperforms existing state-of-the-art baselines while providing explainable results.

中文翻译：

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不确定性感知网络对齐

网络对齐 (NA) 旨在跨多个网络链接公共节点，是许多图挖掘应用程序中的一项基本任务。尽管最近的许多工作取得了进展，但一些基本的限制仍然没有得到适当的内聚解决方法，包括匹配混淆、缺乏对不确定性的正式处理和点对点 (P2P) 约束. 本研究提出了一个新的框架 UANA（不确定性感知网络对齐）来解决现有工作的局限性。通过将节点嵌入为高斯分布而不是点向量，UANA 能够捕获节点表示的不确定性，同时能够将锚节点与可能令人困惑的邻居区分开来。我们通过引入对抗性学习范式来解决 P2P 匹配约束，该范式在训练期间使用跨域生成程序放宽了精确匹配假设，以减少测试节点上的匹配错误。最后，包括可解释性方法来解释我们的 UANA 基于稳健统计得出的对齐结果，这可以解释单个训练样本对 NA 性能的影响，而无需重新训练模型。在真实世界数据集上进行的大量实验表明，UANA 在提供可解释结果的同时显着优于现有的最先进基线。