Stochastic dynamic programming heuristics for influence maximization–revenue optimization

Abstract

The well-known Influence Maximization (IM) problem has been actively studied by researchers over the past decade, with emphasis on marketing and social networks. Existing research have obtained solutions to the IM problem by obtaining the influence spread and utilizing the property of submodularity. This paper is based on a novel approach to the IM problem geared towards optimizing clicks and consequently revenue within an Online Social Network (OSN). Our approach diverts from existing approaches by adopting a novel, decision-making perspective through implementing Stochastic Dynamic Programming (SDP). Thus, we define a new problem Influence Maximization-Revenue Optimization (IM-RO) and propose SDP as a method in which this problem can be solved. The SDP method has lucrative gains for an advertiser in terms of optimizing clicks and generating revenue however, one drawback to the method is its associated "curse of dimensionality" particularly for problems involving a large state space. Thus, we introduce the Lawrence Degree Heuristic (LDH), Adaptive Hill-Climbing (AHC) and Multistage Particle Swarm Optimization (MPSO) heuristics as methods which are orders of magnitude faster than the SDP method whilst achieving near-optimal results. Through a comparative analysis on various synthetic and real-world networks we present the AHC and LDH as heuristics well suited to to the IM-RO problem in terms of their accuracy, running times and scalability under ideal model parameters. In this paper we present a compelling survey on the SDP method as a practical and lucrative method for spreading information and optimizing revenue within the context of OSNs. definition in [22], the IM problem has been actively studied by researchers over the past decade and is not restricted to applications in marketing only but also in healthcare, communication, education, agriculture, and epidemiology [27,30,35,44]. For the IM problem, OSN users are represented in a graph G = (V, E), where the nodes of V represent the users and the edges in E represent the relationships between users. In [22], the problem was first defined as a discrete optimization problem and the term influence of a set of nodes A, denoted by σ (A) was defined to be the expected number of active nodes at the end of a diffusion process, given that A was this initial active node set. According to the work done in [22], the IM problem therefore seeks to determine a parameter K, that is, to find a K − node set of maximum influence; where |A|≤ K.It is an open question to compute this K − node set and expected number of active nodes σ (A) by an efficient method, but very good estimates have been proposed and obtained [6,7,30].This paper provides a novel approach to the IM problem and a formal definition to the model proposed in [19]. We divert from all other existing approaches to IM and adopt a novel decision-making perspective primarily used in shortest paths and resource allocation problems [2,29,37,39]. Thus, we define a new problem, the IM-RO problem and implement SDP as the m...

A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations

“…Although this study provided a strong prediction model compared to the majority of the previous literature, there are many limitations that can help us for future work. First, the proposed model may be extended by other hydrological numerical models [45][46][47][48][49][50]. Finally, our prediction models can be combined with recent advances in swarm intelligence and computational methods [85][86][87][88][89] to improve the accuracy and robustness of our model.…”

“…A first category is a group of studies that developed new prediction models for post-processing [25][26][27][28][29][30][31][32]. Different postprocessing algorithms utilize the second category for post-processing using one or multiple variables [33][34][35][36][37][38][39][40][41][42][43][44][45].…”