Dynamic Influence Maximization with WoM Sensitivity in Blockchain Online Social Network

With the emergence of Web3.0 and blockchain technology, decentralized social platforms like blockchain online social networks (BOSN) have provided enhanced privacy protection while making network regulation more challenging. Influence maximization (IM) has always been a critical technical focus in areas such as viral marketing, network regulation, and opinion monitoring. Currently, a significant amount of existing literature revolves around the optimization of influence maximization problems such as greedy algorithms, heuristic algorithms and classical propagation models. In this paper, we first develop a linear threshold model that is sensitive to the word-of-mouth effect, which appropriately reflects the reputation mechanism of blockchain social networks. Moreover, we have devised an algorithm (CRB) based on community segmentation and community ranking to optimize the operational efficiency of the greedy algorithm. It measures community impact through three attributes: community social score, community density, and community scale. Series experiments are designed to evaluate the performance of the proposed model and algorithm. Our experimental results reveal the impact of the word-of-mouth effects on decentralized social networks and provide evidence for the effectiveness and efficiency of the CRB algorithm.