Online social networks (OSNs) have become an integral part of our daily lives, sha** the
way social relationships evolve. Influence maximization (IM) in OSNs has been widely …

In multi-agent partially observable sequential decision problems with general-sum rewards,
it is necessary to account for the egoism (individual rewards), utilitarianism (social welfare) …

Influence Maximization (IM) is a critical problem in social network analysis and marketing. It
involves identifying a subset of nodes in a social network whose activation or influence can …

As awareness of environmental protection grows, the development of electric vehicles has
emerged as a prominent area of research. Naturally, optimizing Permanent Magnet (PM) …

Designer UGC (user-generated content) communities serve as the epicenter of
contemporary innovation and creativity, offering a platform for a broad design community to …

Influence maximization (IM) has been extensively researched in the information propagation
field and applied in various domains. However, existing studies on the IM have primarily …

Influence maximization (IM) is a prominent topic in the complex network analysis domain,
and its goal is to identify the fewest nodes to achieve the maximum influence in a network …

Graph Convolutional Network (GCN) and its variants emerged as powerful graph deep
learning methods with promising performance on graph analysis tasks. Different variants …

Influence Maximization (IM) stands as a central challenge within the domain of complex
network analysis, with the primary objective of identifying an optimal seed set of a …

Influence maximization (IM) is crucial for recommendation systems and social networks.
Previous research primarily focused on static networks, neglecting the homophily and …