Deep reinforcement learning-based approach for rumor influence minimization in social networks.

Spreading malicious rumors on social networks such as Facebook, Twitter, and WeChat can trigger political conflicts, sway public opinion, and cause social disruption. A rumor can spread rapidly across a network and can be difficult to control once it has gained traction.Rumor influence minimization (RIM) is a central problem in information diffusion and network theory that involves finding ways to minimize rumor spread within a social network. Existing research on the RIM problem has focused on blocking the actions of influential users who can drive rumor propagation. These traditional static solutions do not adequately capture the dynamics and characteristics of rumor evolution from a global perspective. A deep reinforcement learning strategy that takes into account a wide range of factors may be an effective way of addressing the RIM challenge. This study introduces the dynamic rumor influence minimization (DRIM) problem, a step-by-step discrete time optimization method for controlling rumors. In addition, we provide a dynamic rumor-blocking approach, namely RLDB, based on deep reinforcement learning. First, a static rumor propagation model (SRPM) and a dynamic rumor propagation model (DRPM) based on of independent cascade patterns are presented. The primary benefit of the DPRM is that it can dynamically adjust the probability matrix according to the number of individuals affected by rumors in a social network, thereby improving the accuracy of rumor propagation simulation. Second, the RLDB strategy identifies the users to block in order to minimize rumor influence by observing the dynamics of user states and social network architectures. Finally, we assess the blocking model using four real-world datasets with different sizes. The experimental results demonstrate the superiority of the proposed approach on heuristics such as out-degree(OD), betweenness centrality(BC), and PageRank(PR).

Multiscale time-dependent density functional theory: Demonstration for plasmons.

Plasmon properties are of significant interest in pure and applied nanoscience. While time-dependent density functional theory (TDDFT) can be used to study plasmons, it becomes impractical for elucidating the effect of size, geometric arrangement, and dimensionality in complex nanosystems. In this study, a new multiscale formalism that addresses this challenge is proposed. This formalism is based on Trotter factorization and the explicit introduction of a coarse-grained (CG) structure function constructed as the Weierstrass transform of the electron wavefunction. This CG structure function is shown to vary on a time scale much longer than that of the latter. A multiscale propagator that coevolves both the CG structure function and the electron wavefunction is shown to bring substantial efficiency over classical propagators used in TDDFT. This efficiency follows from the enhanced numerical stability of the multiscale method and the consequence of larger time steps that can be used in a discrete time evolution. The multiscale algorithm is demonstrated for plasmons in a group of interacting sodium nanoparticles (15-240 atoms), and it achieves improved efficiency over TDDFT without significant loss of accuracy or space-time resolution.

Early stage P22 viral capsid self-assembly mediated by scaffolding protein: atom-resolved model and molecular dynamics simulation.

Molecular dynamics simulation of an atom-resolved bacteriophage P22 capsid model is used to delineate the underlying mechanism of early stage P22 self-assembly. A dimer formed by the C-terminal fragment of scaffolding protein with a new conformation is demonstrated to catalyze capsomer (hexamer and pentamer) aggregation efficiently. Effects of scaffolding protein/coat protein binding patterns and scaffolding protein concentration on efficiency, fidelity, and capsid curvature of P22 self-assembly are identified.