Medo Matúš, Mariani Manuel Sebastian, Lü Linyuan
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.
Entropy (Basel). 2018 Oct 10;20(10):777. doi: 10.3390/e20100777.
Real networks typically studied in various research fields-ecology and economic complexity, for example-often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.
例如，在生态和经济复杂性等各个研究领域中通常所研究的真实网络，往往呈现出一种嵌套拓扑结构，这意味着高度数节点的邻域倾向于包含低度数节点的邻域。聚焦于嵌套网络，我们研究复杂网络中的链接预测问题，该问题旨在识别缺失链接的可能候选对象。我们发现，一种考虑网络嵌套性的新方法不仅在输入网络具有足够嵌套性时，而且在嵌套结构不完善的网络中，都优于成熟的链接预测方法。我们的研究为在嵌套网络中寻找链接预测的最优方法铺平了道路，这可能对世界贸易和生态网络分析有益。
