学习网络对齐的热扩散
Learning Heat Diffusion for Network Alignment
摘要：
网络在生命科学中非常丰富。突出的挑战包括如何描述网络之间的相似性，以及如何在网络之间集成信息。然而，网络对齐仍然是一个核心算法问题。在这里，我们提出了一种新的学习算法，称为进化热扩散为基础的网络对齐（EDNA），以解决这一挑战。EDNA使用扩散信号作为计算网络之间节点相似性的代理。在一个流行的蛋白质相互作用网络数据集上比较EDNA与最先进的算法，使用四种不同的评估指标，我们实现了（i）最准确的比对，（ii）增加对噪声的鲁棒性，和（iii）优越的缩放能力。EDNA算法是通用的，因为其他可用的网络比对/嵌入可以用作初始基线比对，然后EDNA通过在它们上面运行进化扩散来作为它们的包装器。总之，EDNA优于最先进的网络对齐方法，从而为大规模比较和网络集成奠定了基础。
英文摘要：
Networks are abundant in the life sciences. Outstanding challenges include how to characterize similarities between networks, and in extension how to integrate information across networks. Yet, network alignment remains a core algorithmic problem. Here, we present a novel learning algorithm called evolutionary heat diffusion-based network alignment (EDNA) to address this challenge. EDNA uses the diffusion signal as a proxy for computing node similarities between networks. Comparing EDNA with state-of-the-art algorithms on a popular protein-protein interaction network dataset, using four different evaluation metrics, we achieve (i) the most accurate alignments, (ii) increased robustness against noise, and (iii) superior scaling capacity. The EDNA algorithm is versatile in that other available network alignments/embeddings can be used as an initial baseline alignment, and then EDNA works as a wrapper around them by running the evolutionary diffusion on top of them. In conclusion, EDNA outperforms state-of-the-art methods for network alignment, thus setting the stage for large-scale comparison and integration of networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
