Nestedness is a property of bipartite complex networks that has been shown to characterize the peculiar structure of biological and economical networks. In a nested network, a node of low degree has its neighborhood included in the neighborhood of nodes of higher degree. Emergence of nestedness is commonly due to two different schemes: i) mutualistic behavior of nodes, where nodes of each class have an advantage in associating with each other, such as plant pollination or seed dispersal networks; ii) geographic distribution of species, captured in a so-called biogeographic network where species represent one class and geographical areas the other one. Nestedness has useful applications on real-world networks such as node ranking and link prediction. Motivated by analogies with biological networks, we study the nestedness property of the public Internet peering ecosystem, an important part of the Internet where autonomous systems (ASes) exchange traffic at Internet eXchange Points (IXPs). We propose two representations of this ecosystem using a bipartite graph derived from PeeringDB data. The first graph captures the AS [is member of] IXP relationship which is reminiscent of the mutualistic networks. The second graph groups IXPs into countries, and we define the AS [is present at] country relationship to mimic a biogeographic network. We statistically confirm the nestedness property of both graphs, which has never been observed before in Internet topology data. From this unique observation, we show that we can use node metrics to extract new key ASes and make efficient prediction of newly created links over a two-year period.

翻译：嵌套性是二分复杂网络的一种性质，已被证明能够表征生物和经济网络的独特结构。在嵌套网络中，低度节点的邻居集合被包含于更高程度节点的邻居集合中。嵌套性的涌现通常由两种不同机制导致：（i）节点的互惠行为，即不同类别节点间的关联具有互补优势，例如植物传粉或种子传播网络；（ii）物种的地理分布，表现为所谓生物地理网络，其中物种构成一类节点，地理区域构成另一类节点。嵌套性在现实网络（如节点排序与链路预测）中具有重要应用。受生物网络相似性的启发，本研究分析了公共互联网对等互联生态的嵌套属性——该生态作为互联网的关键组成部分，自治系统（AS）在互联网交换点（IXP）间交换流量。我们利用来自PeeringDB数据的二分图，提出了该生态的两种表征方式：第一种图捕捉AS与IXP的成员关系，类似于互惠网络；第二种图将IXP按国家分组，定义AS与国家间的存在关系，以模拟生物地理网络。我们通过统计检验证实了两种图的嵌套属性——这一现象在互联网拓扑数据中从未被观察到。基于这一独特发现，我们展示了如何利用节点指标提取新的关键AS，并有效预测两年期内新创建的链路。