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 (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 …

Given a social network G and a positive integer k, the influence maximization problem asks
for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest …

In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …

Given a social network G and a constant k, the influence maximization problem asks for k
nodes in G that (directly and indirectly) influence the largest number of nodes under a pre …

Influence Maximization (IM), that seeks a small set of key users who spread the influence
widely into the network, is a core problem in multiple domains. It finds applications in viral …

Research on social networks has exploded over the last decade. To a large extent, this has
been fueled by the spectacular growth of social media and online social networking sites …

Propagation of contagion through networks is a fundamental process. It is used to model the
spread of information, influence, or a viral infection. Diffusion patterns can be specified by a …

If a piece of information is released from a set of media sites, can it spread, in 1 month, to a
million web pages? Can we efficiently find a small set of media sites among millions that can …

Derived from the idea of word-to-mouth advertising and with applying information diffusion
theory, viral marketing attracts wide research interests because of its business value. As an …