Location Promoting Influence Maximization in Social Networks

With the widespread use of GPS-enabled smart devices, online social networks are increasingly integrated with offline local services. People are also more inclined to share their real-time offline location and time information with friends on online platforms. Influence maximization, which involves selecting a set of seed nodes to maximize influence within an online social network, has gained significant attention, especially on location-based social networks. However, most recent studies have focused on users’ discrete check-in data and used bipartite graphs to model the one-way relationship between online users and offline locations, without considering the interactions between different offline location. To address this gap, we introduce the location promoting influence maximization (LPIM), which aims to maximize the number of online users visiting promotional offline location. We also propose the TrajectoryCompetition algorithm, which takes into account users’ movement trajectories to capture their mobility patterns and characteristics, along with the competitive relationships between similar offline locations. Furthermore, the algorithm explores potential connections between online users to better estimate their influence. Extensive experiments conducted on datasets from six real-world cities validate the efficiency and effectiveness of the TrajectoryCompetition algorithm.