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Aqueous zinc ion batteries (AZIBs) are subject to various unwanted side reactions, including dendrite growth, hydrogen evolution, and corrosion passivation, due to the highly active Zn anode. While constructing an artificial interface layer (AIL) can address these problems, the compromise in Zn deposition kinetics is considerable. Herein, F‐doped modified hydrated calcium silicate (F‐CSH) nanosheets are designed to enhance Zn2+ ion deposition kinetics. Combined with theoretical calculations, it is verified that the F‐doped contributes to higher interface adsorption energy with Zn metal and lower ion diffusion barrier, favoring faster charge transfer compared to non‐F‐doped counterparts. Additionally, the strong interaction between zincophilic F and Zn facilitates rapid desolvation of Zn2+ and promotes the deposition between the Zn metal and F‐CSH interphases. As a result, the F‐CSH layer maintains the stability on the Zn surface, enabling fast and reversible Zn deposition. The F‐CSH@Zn anode exhibits a long lifespan of over 2800 h at 5 mA cm−2, while running more than 2500 cycles at a ≈100% Coulombic efficiency. This work highlights the importance of constructing AIL with strong interaction with Zn2+/Zn in improving the interphase kinetics of zinc anode for realizing the stability of AZIBs. [ABSTRACT FROM AUTHOR]

In social network analysis, community detection on plain graphs has been widely studied. With the proliferation of available data, each user in the network is usually associated with additional attributes for elaborate description. However, many existing methods only concentrate on the topological structure and fail to deal with node‐attributed networks. These approaches are incapable of extracting clear semantic meanings for communities detected. In this paper, we combine the topological structure and attribute information into a unified process and propose a novel algorithm to detect overlapping semantic communities. Moreover, a new metric is designed to measure the density of semantic communities. The proposed algorithm is divided into 3 phases. First, we detect local semantic subcommunities from each node's perspective using a greedy strategy on the metric. Then, a supergraph, which consists of all these subcommunities is created. Finally, we find global semantic communities on the supergraph. The experimental results on real‐world data sets show the efficiency and effectiveness of our approach against other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]