The influence maximization problem has gained increasing attention in recent years.
Previous research focuses on the development of algorithms to analyze static social …

Influence maximization is a classic and extensively studied problem with important
applications in viral marketing. Existing algorithms for influence maximization, however …

Influence maximization, defined as finding a small subset of nodes that maximizes spread of
influence in social networks, is NP-hard under both Independent Cascade (IC) and Linear …

Information can be disseminated widely and rapidly through Online Social Networks (OSNs)
with “word-of-mouth” effects. Viral marketing is such a typical application in which new …

Information diffusion in social networks is emerging as a promising solution to successful
viral marketing, which relies on the effective and efficient identification of a set of nodes with …

Influence diffusion estimation is a crucial problem in social network analysis. Most prior
works mainly focus on predicting the total influence spread, ie, the expected number of …

As both social network structure and strength of influence between individuals evolve
constantly, it requires tracking the influential nodes under a dynamic setting. To address this …

Source localization, as a reverse problem of the graph diffusion, bears paramount
significance for a multitude of applications, such as tracking social rumors, detecting …

Online social networks provide convenience for users to propagate ideas, products,
opinions, and many other items that compete with different items for influence spread. How …

Influence maximization in social networks is a classic and extensively studied problem that
targets at selecting a set of initial seed nodes to spread the influence as widely as possible …