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 …

Large-scale multi-objective optimization problems (MOPs) that involve a large number of
decision variables, have emerged from many real-world applications. While evolutionary …

In recent years, various companies have started to shift their data services from traditional
data centers to the cloud. One of the major motivations is to save on operational costs with …

The rapid development of community detection algorithms, while serving users in social
networks, also brings about certain privacy problems. In this work, we study community …

There are many multi-objective optimization problems (MOPs) in real life that contain a large
number of decision variables, such as auto body parts design, financial investment …

In the present-days complex networks modeled on real-world data contain millions of nodes
and billions of links. Identifying super spreaders in such an extensive network is a …

In social networks, decision makers of group decision-making (GDM) can have interactive
behaviors with different individual relationships. With large-scale group participation …

Multi-objective optimization problems (MOPs) containing a large number of decision
variables, which are also known as large-scale multi-objective optimization problems …

Influence maximization is an important research area in social network analysis where
researchers are concerned with detecting influential nodes. The detection of influential …

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to
handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of …