Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …

Advertising using the word-of-mouth effect is quite effective in promoting products. In the last
decade, there has been intensive research studying the influence maximization problem in …

Balanced influence maximization aims to balance the influence maximization of multiple
different entities in social networks and avoid the emergence of filter bubbles and echo …

Influence maximization is a crucial issue for mining the deep information of social networks,
which aims to select a seed set from the network to maximize the number of influenced …

The quality of seeds is crucial for influence maximization in social networks. Seed selection
algorithms based on deep reinforcement learning (DRL) combine the representation …

In this paper, we address the problem of revenue maximization (RM) for multi-grade
products in social networks by considering pricing, seed selection, and coupon distribution …

Competitive Influence Maximization (CIM) involves entities competing to maximize influence
in online social networks (OSNs). Current Deep Reinforcement Learning (DRL) methods in …

The Competitive Influence Maximization (CIM) problem involves entities competing to
maximize influence in online social networks (OSNs). While Deep Reinforcement Learning …

A significant amount of society's infrastructure can be modeled using graph structures, from
electric and communication grids, to traffic networks, to social networks. Each of these …

Social networks possess a dual nature, serving as platforms for rapid dissemination of
innovations as well as widespread circulation of rumors. The proliferation of false …