Suggestive Node Tracking Enterprising Reception Network Using Stream Influence Maximization

Abstract

Greedy algorithm is used for mining top-K influential nodes. It has two components dividing the mobile social network into several communities by taking into account information diffusion and selecting communities to find influential nodes by a dynamic programming. Location Based community Greedy algorithm is used to find the influence node based on location and consider the influence propagation within particular area. Influence Maximization (IM), which selects a set of k users to maximize the influence spread over a social network is a fundamental problem in a wide range of applications such as viral marketing and network monitoring. We define a novel IM query named Stream Influence Maximization (SIM) on social streams. Technically SIM adopts the sliding window model and maintains a set of k seeds with the largest influence value over the most recent social actions.  We propose the Influential Checkpoints (IC) framework to facilitate continuous SIM query processing.  We propose a model of dynamic rumor influence reduction with user expertise (DRIMUX).Our goal is to cut back the influence of the rumor by block an exact set of nodes. A dynamic propagation model considering every the worldwide quality and individual attraction of the rumor is given supported realistic state of affairs. To boot altogether completely different from existing problems with influence reduction, Our goal is to cut back the influence of the rumor block an exact set of nodes The prior works have shown that the rumor blocking problem is approximated within a factor of (1 − 1/e) by a classic greedy algorithm combined with Monte Carlo simulation. Unfortunately, the Monte Carlo simulation based methods are time consuming and the existing algorithms either trade performance guarantees for practical efficiency. We present a randomized approximation algorithm which is provably superior to the state-of-the art methods with respect to running time.