论文标题

关于使用双感应相似性的渐进网络对齐的幂

On the Power of Gradual Network Alignment Using Dual-Perception Similarities

论文作者

Park, Jin-Duk, Tran, Cong, Shin, Won-Yong, Cao, Xin

论文摘要

网络对齐(NA)是基于网络结构和节点属性之间找到两个网络之间节点的对应关系的任务。我们的研究的动机是,由于大多数现有的NA方法试图一次发现所有节点对,因此它们不会通过临时发现节点对应关系来实现富集的信息,从而更准确地在节点匹配过程中找到下一个对应关系。为了应对这一挑战,我们提出了一种Grad-Align,这是一种新的NA方法,它通过充分利用表现出强烈一致性的节点对逐渐发现节点对,在逐渐匹配的早期阶段很容易发现。具体而言,Grad-Align首先基于图形神经网络生成两个网络的节点嵌入,以及我们的层次重建损失,这是在捕获一阶和高阶邻域结构的基础上构建的。然后,通过计算双感应相似性度量(包括多层嵌入相似性以及TVERSKY相似性)来逐渐对齐节点,这是使用适用于具有不同规模的网络的TVERSKY索引的不对称集合相似性。此外,我们将边缘增强模块纳入毕业对准中,以增强结构一致性。通过使用现实世界和合成数据集的全面实验,我们从经验上证明,毕业生对象始终优于最先进的NA方法。

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods.

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