Label propagation is a promising engine for community detection, widely utilized in anomaly analysis and recommendation systems. However, traditional label propagation algorithms struggle with large-scale networks due to poor stability, slow convergence, and low accuracy. Despite its advancements, the state-of-the-art variant LBLD (Local Balanced Label Diffusion) also faces challenges, such as limited utilization of similarity and a rudimentary approach to constructing the initial community framework, which diminishes the quality of the final results.To address these limitations, we introduce PNLP, a novel similarity-based label propagation algorithm. The key innovations of PNLP include: (i) Integrating similarity throughout the entire process of label propagation, enhancing the robustness and accuracy of community detection. (ii) Defining a Node Importance metric (NI) to provide a more comprehensive assessment of node influence. (iii) Introducing a novel method for identifying rough seed nodes and constructing a tighter initial framework for communities using conductance.Furthermore, PNLP employs a similarity-based influence metric to guide the label updating process, ensuring a more refined and accurate detection of community structures. Extensive experiments conducted on both real-world and synthetic networks demonstrate the superior performance of PNLP, which significantly outperforms established methods such as Infomap, Louvain, Leiden, LPA, and LBLD in terms of accuracy by factors of up to 1.39 times, 1.48 times, 1.44 times, 2.05 times and 1.25 times, respectively.

The influence maximization (IM) problem serve as a promising engine for the comprehension of social multimedia. However, current IM solutions often rely on fixed sampling and diffusion probabilities, which are insufficient to cater to the dynamic needs of online advertising, particularly in targeting users with diverse topic preferences. In addition, existing topic-aware IM solutions for online advertising are either time-consuming during the online query phase or burdened with substantial time and space overhead during the offline index construction phase. To solve the above problems, we propose GraphHash, a topic-aware target influence maximization solution that adopts a combination of offline and online phase. GraphHash constructs offline indexes by utilizing hash-based topic probabilities and selects the seed set with the maximal influence through intelligent indexing technique. In addition, we extend GraphHash using bottom-k sketching technology and apply it to different real-world graphs, achieving significant improvements of orders of magnitude over state-of-the-art baselines.