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 …

The problem of influence maximization, ie, mining top-k influential nodes from a social
network such that the spread of influence in the network is maximized, is NP-hard. Most of …

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 an important problem of finding a small subset of nodes in a social
network, such that by targeting this set, one will maximize the expected spread of influence …

Influence Maximization, which selects a set of k users (called seed set) from a social network
to maximize the expected number of influenced users (called influence spread), is a key …

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 (IM) is the problem of finding a small subset of nodes in a social
network so that the number of nodes influenced by this subset can be maximized. Influence …

In recent years, due to the surge in popularity of social-networking web sites, considerable
interest has arisen regarding influence maximization in social networks. Given a social …

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 …

In a social network, information runs from word-of-mouth based on the relationship of the
users. The influence maximization is to find a limited number of initial users (nodes) to …