Heterogeneous network flow and Petri nets characterize multilayer complex networks.
Heterogeneous network flow and Petri nets characterize multilayer complex networks.
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DOI：
10.1038/s41598-022-07249-6
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发表时间：
2022-03-03
影响因子：
4.6
通讯作者：
Strbac G
中科院分区：
文献类型：
--
作者：
Ademovic Tahirovic A;Angeli D;Strbac G
相互作用的子系统通常用网络来描述，在大多数自然或工程系统中发现的多模态行为最近以多层网络的形式扩展。由于多模态交互往往不是由网络拓扑结构单独决定的，并且可以表现为跨层信息交换的形式，因此多层网络流变得相关的进一步感兴趣。基本原理可以在大多数相互作用的子系统中找到，其中可以在例如，化学过程、能源网络、物流、金融或依赖于守恒定律的任何其他形式的转换过程。为此，提出了异构网络流的形式化概念，作为与网络流理论相一致的多层流函数。在此基础上，利用Petri网的框架建立了并发事件系统的动态等价模型，作为并发事件系统的基线模型。应用所产生的多层拉普拉斯流和流中心，沿着与图学习为基础的推理多层关系的多模态数据。在合成数据上，所提出的框架证明了多模态流推导在关键部件识别中的益处。它也显示在多模态时间序列的关系推理（基于学习的函数逼近）的适用性。在真实世界的数据上，所提出的框架提供了对美国经济活动的多模态流解释，揭示了潜在的经验稳态概率分布以及固有的网络（经济）鲁棒性。
Interacting subsystems are commonly described by networks, where multimodal behaviour found in most natural or engineered systems found recent extension in form of multilayer networks. Since multimodal interaction is often not dictated by network topology alone and may manifest in form of cross-layer information exchange, multilayer network flow becomes of relevant further interest. Rationale can be found in most interacting subsystems, where a form of multimodal flow across layers can be observed in e.g., chemical processes, energy networks, logistics, finance, or any other form of conversion process relying on the laws of conservation. To this end, the formal notion of heterogeneous network flow is proposed, as a multilayer flow function aligned with the theory of network flow. Furthermore, dynamic equivalence is established with the framework of Petri nets, as the baseline model of concurrent event systems. Application of the resulting multilayer Laplacian flow and flow centrality is presented, along with graph learning based inference of multilayer relationships over multimodal data. On synthetic data the proposed framework demonstrates benefits of multimodal flow derivation in critical component identification. It also displays applicability in relationship inference (learning based function approximation) on multimodal time series. On real-world data the proposed framework provides, among others, multimodal flow interpretation of U.S. economic activity, uncovering underlying empirical steady state probability distribution, as well as inherent network (economic) robustness.
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影响因子：
1.9
作者：
CRAVEN, P;WELLMAN, B
通讯作者：
WELLMAN, B
影响因子：
12.5
作者：
De Domenico, Manlio;Lancichinetti, Andrea;Rosvall, Martin
通讯作者：
Rosvall, Martin
影响因子：
19.6
作者：
De Domenico, Manlio;Granell, Clara;Arenas, Alex
通讯作者：
Arenas, Alex
DOI：
10.1073/pnas.1318469111
发表时间：
2014-06-10
影响因子：
11.1
作者：
De Domenico, Manlio;Sole-Ribalta, Albert;Arenas, Alex
通讯作者：
Arenas, Alex
影响因子：
12.5
作者：
De Domenico, Manlio;Sole-Ribalta, Albert;Arenas, Alex
通讯作者：
Arenas, Alex