论文标题
随机步行在随着时变的网络中
Random walks in time-varying networks with memory
论文作者
论文摘要
网络上的随机步行过程在理解节点的重要性及其相似性方面发挥了基本作用，该节点的相似性已被广泛应用于Pagerank，信息检索和社区检测等。事实证明，个人的记忆被证明对于影响网络的网络演化和动态过程非常重要。在此手稿中，我们通过考虑个人的记忆来研究扩展活动驱动网络模型的随机行程过程。我们分析了当随机步行的过程和网络演化的过程的时间尺度相当时，个人的内存如何影响网络上的随机步行过程。在长期进化的限制下，我们得出了分析解决方案，用于固定状态WA的分布以及随机步行过程的平均第一邮算时间（MFPT）。我们发现，与无内存活动驱动的模型相比，个人的内存增强了度分布的波动，从而降低了收集步道的节点的能力，尤其是在大型活动中，并且延迟了平均的第一通道时间。实际网络上的结果还支持人工网络的理论分析。
Random walks process on networks plays a fundamental role in understanding the importance of nodes and the similarity of them, which has been widely applied in PageRank, information retrieval, and community detection, etc. Individual's memory has been proved to be important to affect network evolution and dynamical processes unfolding on the network. In this manuscript, we study the random-walk process on extended activity driven network model by taking account of individual's memory. We analyze how individual's memory affects random-walk process unfolding on the network when the timescales of the processes of the random walk and the network evolution are comparable. Under the constraints of long-time evolution, we derive analytical solutions for the distribution of stationary state Wa and the mean first-passage time (MFPT) of the random-walk process. We find that, compared with the memoryless activity-driven model, individual's memory enhances the fluctuation of degree distribution, which reduces the capability of gathering walkers for nodes, especially with large activity and delays the mean first-passage time. The results on real networks also support the theoretical analysis with artificial networks.
