论文标题
不断发展的网络中出生
Birth-Burst in Evolving Networks
论文作者
论文摘要
复杂网络的演变受增长的规则和内部属性的控制。大多数不断发展的网络模型（例如优惠依恋）强调了增长的策略，同时忽略了单个节点的特征。在这项研究中，我们分析了一个广泛研究的网络：不断发展的蛋白质 - 蛋白质相互作用（PPI）网络。我们发现了单个节点的关键贡献，特别是在其出生时发生。具体而言，一个节点天生具有适应性值 - 测量其内在意义。当引入具有较大适应性的节点时，相应地确定了相应的高生育学位，从而导致网络中的连通性突然提高。这些大（集线器）节点的程度分数不会随着网络的发展而衰减，同时保持持续的影响。在这里，我们开发了诞生模型，即适应性模型的改编，以模拟网络演化中的学位阶段和相位转移。
The evolution of complex networks is governed by both growing rules and internal properties. Most evolving network models (e.g. preferential attachment) emphasize on the growing strategy, while neglecting the characteristics of individual nodes. In this study, we analyzed a widely studied network: the evolving protein-protein interaction (PPI) network. We discovered the critical contribution of individual nodes, occurring particularly at their birth. Specifically, a node is born with a fitness value - a measurement of its intrinsic significance. Upon the introduction of a node with a large fitness into the network, a corresponding high birth-degree is determined accordingly, leading to an abrupt increase of connectivity in the network. The degree fraction of these large (hub) nodes does not decay away with the network evolution, while keeping a constant influence over the lifetime. Here we developed the birth-burst model, an adaptation of the fitness model, to simulate degree-burst and phase-transition in the network evolution.
