With the rapid expansion of the internet, IP geolocation has become crucial for network security, content delivery, and compliance. However, existing methods struggle with dynamic, noisy networks, especially in handling topology changes, noisy data, and efficient node selection. To address these, we introduce PINGeo, a novel framework based on graph convolutional networks. PINGeo combines two innovations: (1) adaptive graph pruning using node importance metrics to retain critical nodes, optimizing both efficiency and accuracy, and (2) Gaussian noise perturbation to enhance robustness by simulating real-world fluctuations. By applying these methods, PINGeo improves geolocation accuracy and model robustness in noisy environments. Experimental results on public datasets show that PINGeo outperforms state-of-the-art methods in accuracy and robustness, offering a promising solution for robust IP geolocation.

Instead of using a standard diffusion model, most diffusion-based speech enhancement methods additionally incorporate a mean interpolation strategy into the diffusion process. However, the role and impact of this strategy remain unclear. In this study, we investigate mean interpolation from a data augmentation perspective, demonstrating that it serves as a specific form of distribution augmentation and provides a unified formula for mean interpolation, which encompasses current variations of such strategy. Building on this insight, we propose DANS (Distribution Augmentation with Noise Shuffling), an advanced distribution augmentation method that further expands the training distribution through noise-shuffling techniques. Experimental results show that DANS consistently outperforms existing methods in matched, cross-dataset, and low-data scenarios. Codes are publicly available at https://github.com/ICDM-UESTC/DANCE.