The evaluation and application of node influence in dynamic networks based on evolving communities

Abstract

AbstractThe network connectivity in dynamic networks depends on a small number of highly mobile nodes. Identifying the influential nodes is one of the most engaging challenges for mobile applications, such as data offloading or worm propagation control. Reachability is an important metric to uncover node influence. Both TRGs (temporal reachability graphs) and CJEGs (critical journey evolving graphs) provide approaches to calculate reachability. Nevertheless, these approaches are only for epidemic scenarios. In practice, due to node privacy or limited battery life, message transmission is, to a certain extent, a probabilistic dissemination. Accordingly, reachability is difficult to exactly determine. As a structure of tight‐knit nodes, a community is born of a coarse‐grained estimation of reachability under a probabilistic propagation scenario. Based on an existing overlapping community detection framework, ie, AFOCS (an unsupervised machine learning algorithm), we propose an evolving overlapping community detection algorithm, ie, EFOCS, and further developed a metric, ie, OR_CEN, to estimate the reachability under the probabilistic propagation scenario. A content delivery experiment showed that OR_CEN accurately reveals the influence of nodes in dynamic networks. Based on OR_CEN, we also propose several target set selection algorithms and discuss their application in mobile data offloading. Analysis and simulation experiments indicated that CBS_OR, the target set selection algorithm based on OR_CEN centrality, has more advantages in scalability and distributed computing than CBS_AFOCS (an algorithm based on an aggregated AFOCS community), TRG_GREEDY (a greedy algorithm based on TRGs) and RANDOM (an algorithm based on a random selection strategy). Moreover, CBS_OR exhibited a significant better offloading effect than the algorithms mentioned earlier.