Sensibly highlighting the hidden structures of many real-world networks has attracted
growing interest and triggered a vast array of techniques on what is called nowadays …

In pedagogy, teachers usually separate mixed-level students into different levels, treat them
differently and teach them in accordance with their cognitive and learning abilities. Inspired …

The community detection in dynamic networks is essential for important applications such as
social network analysis. Such detection requires simultaneous maximization of the …

Mobile crowdsourcing is an emerging crowdsourcing paradigm, which generates large-
scale sensing tasks and sensing data. One of the major issues in mobile crowdsourcing is …

Large-scale optimization has become a significant yet challenging area in evolutionary
computation. To solve this problem, this paper proposes a novel segment-based …

Evolutionary algorithms have been demonstrated to be very competitive in the community
detection for complex networks. They, however, show poor scalability to large-scale …

Over the past couple of decades, the research area of network community detection has
seen substantial growth in popularity, leading to a wide range of researches in the literature …

Large-scale optimization with high dimensionality and high computational cost becomes
ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed …

The human brain develops rapidly in the first postnatal year, in which rewired functional
brain networks could shape later behavioral and cognitive performance. Resting-state …

Designing multiobjective evolutionary algorithms (MOEAs) for community detection in
complex networks has attracted much attention of researchers recently. However, most of …