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Preferential attachment (PA) network models have a wide range of applications in various scientific disciplines. Efficient generation of large-scale PA networks helps uncover their structural properties and facilitate the development of associated analytical methodologies. Existing software packages only provide limited functions for this purpose with restricted configurations and efficiency. We present a generic, user-friendly implementation of weighted, directed PA network generation with R package wdnet. The core algorithm is based on an efficient binary tree approach. The package further allows adding multiple edges at a time, heterogeneous reciprocal edges, and user-specified preference functions. The engine under the hood is implemented in C++. Usages of the package are illustrated with detailed explanation. A benchmark study shows that wdnet is efficient for generating general PA networks not available in other packages. In restricted settings that can be handled by existing packages, wdnet provides comparable efficiency. 

Monolayer molybdenum di-selenide (1L-MoSe 2 ) stands out in the transition metal dichalcogenide family of materials as an outlier where optical generation of valley polarization is inefficient. Here we show that using charge doping in conjunction with an external magnetic field, the valley polarization of 1L-MoSe 2 can be controlled effectively. Most remarkably, the valley polarization can be tuned to negative values, where the higher energy Zeeman mode emission is more intense than the lower energy one. Our experimental observations are interpreted with valley-selective exciton-charge dressing that manifests when gate induced doping populates predominantly one valley in the presence of Zeeman splitting.