Influence maximization (IM), as an essential problem in social network analysis, can identify
a minimum group of the influential nodes to maximize the spread of information on the …

To maximize the influence across multiple heterogeneous networks, we propose an
innovative cross-network learning model to study the influence maximization problem from …

Influence Maximization (IM) is the problem of finding a small set of highly influential users in
the social networks. The influence spreads according to an explicit influence propagation …

Given a social graph, the problem of influence maximization is to determine a set of nodes
that maximizes the spread of influences. While some recent research has studied the …

In many real world applications of influence maximization, practitioners intervene in a
population whose social structure is initially unknown. This poses a multiagent systems …

With the boom in technologies and mobile networks in recent years, online social networks
have become an integral part of our daily lives. These virtual networks connect people …

The influence maximization problem aims at finding a subset of seed users who can
maximize the spread of influence in online social networks (OSNs). Existing works mostly …

In this paper, we study the location aware influence maximization problem, which finds a
seed set to maximize the influence spread on targeted users for a given query. In particular …

Influence maximization is a well-studied problem that asks for a small set of influential users
from a social network, such that by targeting them as early adopters, the expected total …

Influence maximization, ie to maximize the influence spread in a social network by finding a
group of influential nodes as small as possible, has been studied widely in recent years …