Yaveroğlu Ömer Nebil, Malod-Dognin Noël, Davis Darren, Levnajic Zoran, Janjic Vuk, Karapandza Rasa, Stojmirovic Aleksandar, Pržulj Nataša
Department of Computing, Imperial College London, UK.
Computer Science Department, University of California, Irvine, USA.
Sci Rep. 2014 Apr 1;4:4547. doi: 10.1038/srep04547.
Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.
用于分析复杂网络的精密方法有望对几乎所有科学学科都大有裨益，但我们尚未掌握这些方法。在这项工作中，我们取得了根本性的方法进展来纠正这一状况。我们发现，网络中节点所扮演的少数角色之间的相互作用能够表征网络结构，还能给出清晰的现实世界解释。基于这一见解，我们开发了一个用于分析和比较网络的框架，该框架优于所有现有框架。我们通过揭示看似不相关的网络（如脸书、代谢和蛋白质结构网络）之间的新关系来展示其优势。我们还用它来追踪世界贸易网络的动态，结果表明，一个国家在非贸易国家之间充当经纪人的角色预示着经济繁荣，而处于边缘角色则与贫困相关。这一结果虽然直观，但所有现有框架都未能得出。最后，我们的方法将网络拓扑转化为日常语言，使网络分析更贴近领域科学家。
