复杂网络内外节点可达性的非线性动力学研究
Inward and Outward Node Accessibility in Complex Networks as Revealed by Non-Linear Dynamics
摘要：
在这项工作中，个人节点的外向和内向accessibility的定义和他们的应用潜力说明相对于6种不同类型的网络的调查。向外可访问性量化了单个节点通过随机游走访问其他节点的潜力。向内可访问性度量给定节点被其他节点访问的可能性。这两个向内和向外accessibility测量相对于连续的时间步长沿步行，提供了一个有趣的手段，表征的瞬态非线性动态的访问。这里考虑自避免行走，因为它们更有目的性，而且必然是有限的（不像传统的随机行走）。结果表明，网络的内向可达性值远小于外向可达性值，内向可达性值与节点度高度相关，而外向可达性值与节点度无关，地理网络中可达性的显著行为，无标度网络中枢纽节点的优势，以及路径规则模型的辅助的增强的均匀性。还研究了预测给定节点的可访问性的可能性，在其各自的程度。向内和向外可访问性的概念以及获得的几个结果对应用于几个实时问题具有几种影响和潜力，包括疾病传播、万维网冲浪、蛋白质相互作用、皮质网络和网络攻击等。
英文摘要：
In this work, the outward and inward accessibilities of individual nodes are defined and their potential for application is illustrated with respect to the investigation of 6 different types of networks. The outward accessibility quantifies the potential of an individual node for accessing other nodes through random walks. The inward accessibility measures the potential of a given node of being accessed by other nodes. Both the inward and outward accessibilities are measured with respect to successive time steps along the walks, providing an interesting means for the characterization of the transient non-linear dynamics of accessibility. Self-avoiding walks are considered here because they are more purposive and necessarily finite (unlike traditional random walks). The results include the identification of the fact that the inward values tend to be much smaller than the outward values, the tendency of the inward accessibility to be highly correlated with the node degree while the outward values are mostly uncorrelated with that measurements, the distinct behavior of the accessibility in geographical networks, the dominance of hubs in scale free networks, as well as the enhanced uniformity of the accessibilities for the path-regular model. Also investigated was the possibility to predict the accessibility of a given node in terms of its respective degree. The concepts of inward and outward accessibility, as well as the several obtained results, have several implications and potential for applications to several real-time problems including disease spreading, WWW surfing, protein interaction, cortical networks and network attacks, among others.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
