Influence Maximization (IM) is the problem of finding a seed set composed of k nodes that
maximize their influence spread over a social network. Kempe et al. showed the problem to …

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 …

The influence maximization is the problem of how to find a small subset of nodes (seed
nodes) that could maximize the spread of influence in social network. However, it proved to …

Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a
social network that could maximize the spread of influence. In this paper, we study the …

Influence maximization has attracted a lot of attention due to its numerous applications,
including diffusion of social movements, the spread of news, viral marketing and outbreak of …

This paper deals with a novel research work about a new efficient approximation algorithm
for influence maximization, which was introduced to maximize the benefit of viral marketing …

Kempe et al.[4](KKT) showed the problem of influence maximization is NP-hard and a
simple greedy algorithm guarantees the best possible approximation factor in PTIME …

Influence maximization is to extract a small set of nodes from a social network which
influences the propagation maximally under a cascade model. In this paper, we propose a …

Influence maximization (IM) is the problem of finding a small subset of nodes (seed nodes)
in a social network that could maximize the spread of influence. Despite the progress …

Influence Maximization aims to find the top-K influential individuals to maximize the
influence spread within a social network, which remains an important yet challenging …