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 …

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

Background Numerous centrality measures have been introduced to identify “central” nodes
in large networks. The availability of a wide range of measures for ranking influential nodes …

Influence maximization is a classic optimization problem studied in the area of social
network analysis and viral marketing. Given a network, it is defined as the problem of finding …

Influence maximization problem aims to select a subset of k most influential nodes from a
given network such that the spread of influence triggered by the seed set will be maximum …

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 …

Influence Maximization is an important problem in social networks, and its main goal is to
select some most influential initial nodes (ie, seed nodes) to obtain the maximal influence …

Online social networks play an essential role in propagating information, innovation, and
ideas via word-of-mouth spreading. This word-of-mouth phenomenon leads to a …

In the era of advanced mobile technology, freedom of expression over social media has
become prevalent among online users. This generates a huge amount of communication …

Hypergraphs that can depict interactions beyond pairwise edges have emerged as an
appropriate representation for modeling polyadic relations in complex systems. With the …