The influence maximization (IM) problem identifies the subset of influential users in the
network to provide solutions for real-world problems like outbreak detection, viral marketing …

Influence Maximization (IM), 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 …

To meet the requirement of social influence analytics in various applications, the problem of
influence maximization has been studied in recent years. The aim is to find a limited number …

Given a social network with diffusion probabilities as edge weights and a positive integer k,
which k nodes should be chosen for initial injection of information to maximize the influence …

Influence maximization (IM) is the process of choosing a set of seeds from a social network
so that the most individuals will be influenced by them. Calculating the social effect of a …

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

Advertising in social network has become a multi-billion dollar industry. A main challenge is
to identify key influencers who can effectively contribute to the dissemination of information …

Motivated by the application of viral marketing, the topic-aware influence maximization (TIM)
problem has been proposed to identify the most influential users under given topics. In …

Influence maximization is an important technique for its significant value on various social
network applications, such as viral marketing, advertisement, and recommendation …

In this work, we present the Klout Score, an influence scoring system that assigns scores to
750 million users across 9 different social networks on a daily basis. We propose a …