Temporal network alignment via GoT-WAVE
Temporal network alignment via GoT-WAVE
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DOI：
10.1093/bioinformatics/btz119
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发表时间：
2019-09-15
期刊：
影响因子：
5.8
通讯作者：
Silva, Fernando
中科院分区：
文献类型：
--
作者：
Aparicio, David;Ribeiro, Pedro;Silva, Fernando
动机：网络对齐（NA）发现两个网络之间的保守区域。NA方法优化了节点守恒和边缘守恒。动态石墨烯度向量是一种最先进的动态NC测量，用于时间网络的最快和最准确的NA方法：dynwave。在这里，我们使用了一种不同的基于石墨的时间节点相似性度量——石墨轨道转换（got），作为dynamwave中的一种新的动态NC度量，从而产生了GoT-WAVE。结果：在合成网络上，GoT-WAVE将dynwave的准确率提高了30%，速度提高了64%。在实际网络中，当只对动态NC进行优化时，这两种方法是互补的。此外，只有GoT-WAVE支持有向边。因此，GoT-WAVE算法是一种很有前途的动态NC优化算法。我们为GoT-WAVE提供了一个用户友好的用户界面和源代码。
Motivation: Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE.Results: On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE.