Safeguarding crucial nodes provides a direct approach to impede the dissemination of
malicious information in complex networks, such as the Internet. However, determining the …

Federated learning enables collaborative AI training across organizations without
compromising data privacy. Decentralized federated learning (DFL) improves this by offering …

Critical nodes in networks are extremely vulnerable to malicious attacks to trigger negative
cascading events such as the spread of misinformation and diseases. Therefore, effective …

The function or performance of a network is strongly dependent on its robustness, which
quantifies the ability of the network to continue functioning under perturbations. While a wide …

Understanding the network structure is critical for controlling and mitigating the spread of
infectious diseases. Removing many important links to control the spread of infectious …

For a connected graph G=(V, E) with n nodes, m edges, and Laplacian matrix L, a grounded
Laplacian matrix L (S) of G is a (nk)×(nk) principal submatrix of L, obtained from L by …

When an epidemic occurs in a network, finding the important links and cutting them off is an
effective measure for preventing the spread of the epidemic. Traditional methods that …

The grounded Laplacian matrix L− S of a graph G=(V, E) with n=| V| nodes and m=| E| edges
is a (n− s)×(n− s) submatrix of its Laplacian matrix L, obtained from L by deleting rows and …

The operation of creating edges has been widely applied to optimize relevant quantities of
opinion dynamics. In this paper, we consider a problem of polarization optimization for the …

Network connectivity minimization is a fundamental problem in controlling the spread of
epidemics and facilitating information propagation in social networks. The problem aims to …