iNEAT: Incomplete Network Alignment
iNEAT: Incomplete Network Alignment
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DOI：
10.1109/icdm.2017.160
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Si Zhang;Hanghang Tong;Jie Tang;Jiejun Xu;Wei Fan
中科院分区：
文献类型：
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作者：
Si Zhang;Hanghang Tong;Jie Tang;Jiejun Xu;Wei Fan
网络对齐和网络完成是许多高影响力图挖掘应用程序背后的两个基本基石。最先进的技术一直在并行处理这些任务。在本文中，我们认为，网络对齐和完成本质上是相互补充的，因此建议共同解决它们，使这两个任务可以受益于对方。我们从优化的角度来阐述它，并提出了一个有效的算法iNEAT来解决它。首先（对齐精度），我们的方法受益于更高质量的输入网络，同时减轻了由完成任务本身引入的错误推断链接的影响。第二（对齐效率），由于完整网络和对齐矩阵的低秩结构，对齐可以显着加速。大量的实验证明了我们的算法的性能。
Network alignment and network completion are two fundamental cornerstones behind many high-impact graph mining applications. The state-of-the-arts have been addressing these tasks in parallel. In this paper, we argue that network alignment and completion are inherently complementary with each other, and hence propose to jointly address them so that the two tasks can benefit from each other. We formulate it from the optimization perspective, and propose an effective algorithm iNEAT to solve it. The proposed method offers two distinctive advantages. First (Alignment accuracy), our method benefits from higher-quality input networks while mitigates the effect of incorrectly inferred links introduced by the completion task itself. Second (Alignment efficiency), thanks to the low-rank structure of the complete networks and alignment matrix, the alignment can be significantly accelerated. The extensive experiments demonstrate the performance of our algorithm.