Cross-linked structure of network evolution
Cross-linked structure of network evolution
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DOI：
10.1063/1.4858457
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发表时间：
2014-03-01
期刊：
影响因子：
2.9
通讯作者：
Grafton, Scott T.
中科院分区：
文献类型：
--
作者：
Bassett, Danielle S.;Wymbs, Nicholas F.;Grafton, Scott T.
我们通过网络的交联结构来研究网络共同进化的时间共同变化，为此，我们利用了超图的形式主义来将交联结构映射回网络节点。我们详细研究了两组时间网络数据。在耦合非线性振荡器的网络中，由临时重量重量的网络边缘组成的超音正在发现振荡器社区内外边缘权重动力学的驾驶共进化模式。在人的大脑中，代表学习过程中大脑活动时间变化的网络表现出早期的共同进化，然后随后逐步解决。随后的超边缘大小的减小与自主子图的出现一致，该子图不再取决于网络的其他部分。我们对真实和合成网络的结果给出了令人难以置信的证明，表明了交联结构在真实和合成动力学系统中发现意外的共进化属性的能力。反过来，这说明了分析交联用于研究时间网络结构的实用性。 （c）2014 AIP Publishing LLC。
We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks. (C) 2014 AIP Publishing LLC.