The influence maximization problem has attracted increasing attention in previous studies.
Recent years have witnessed an enormous interest in the modeling, performance …

In recent years, there has been a notable surge in research on machine learning techniques
for combinatorial optimization. It has been shown that learning-based methods outperform …

Networked systems present the correlative relation between entities, and the related
knowledge excavation problem has been greatly emphasized in the last decade. The …

The influence maximization problem is a research hotspot on social networks, which
involves selecting a set of nodes as seeds to maximize the influence spread. The robust …

In our modern digital era, social networks have seamlessly integrated into the fabric of our
daily lives. These digital platforms serve as vital channels for communication, exchanging …

In recent years, one of the prominent research areas in the complex network field has been
the Influence Maximization Problem. This problem focuses on selecting seed sets to achieve …

Nowadays, it has been demonstrated that graph neural networks (GNNs) are a powerful tool
for graph representation learning. However, most GNN models are shallow ones because …

Realistic networks constantly face complex environments and are vulnerable to external
disturbances that can damage their structure and disrupt information flow. These challenges …

To optimize short video data dissemination in edge networks, we propose a novel 3-layered
network model. Based on the model, we investigate the optimal edge caching node …

In the field of research in social networks, the influence maximization problem has captured
an abundance of attention. Mostly, influence maximization is performed by selecting the best …