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.

This paper exploits self-supervised learning (SSL) to learn more accurate and robust representations from the user-item interaction graph. Particularly, we propose a novel SSL model that effectively leverages contrastive multi-view learning and pseudo-siamese network to construct a pre-training and post-training framework. Moreover, we present three graph augmentation techniques during the pre-training stage and explore the effects of combining different augmentations, which allow us to learn general and robust representations for the GNN-based recommendation. Simple experimental evaluations on real-world datasets show that the proposed solution significantly improves the recommendation accuracy, especially for sparse data, and is also noise resistant.

Graph Neural Networks (GNNs) and its many variants have recently been successfully utilized to tackle various recommendation tasks. Although effective, existing methods still face several limitations. First, supervision signal sparsity makes it difficult for them to learn high-quality representations and optimize the model parameters. Second, the learned representations are vulnerable to noisy interactions, as the neighborhood aggregation scheme increases the impact of observed edges. To alleviate these issues, we exploit self-supervised learning (SSL) to learn more accurate and robust representations from the user-item interaction graph. Particularly, we propose a novel SSL model that effectively integrates contrastive multi-view learning and pseudo-siamese network to construct a pre-training and post-training framework. Moreover, we present three data augmentation techniques during the pre-training stage and explore the effects of combining different augmentations, which allow us to learn more general and robust representations for the recommendation. Experimental evaluations on two real-world datasets show that the proposed solution significantly improves the recommendation accuracy, especially for sparse supervision signal, and is also noise resistant.

Most existing studies that employ social-trust information to solve the data sparsity issue in recommender systems assume that socially connected users have equal influence on each other. However, this assumption does not hold in practice since users and their friends may not have similar interests because social connections are multifaceted and exhibit heterogeneous strengths in different scenarios. Therefore, estimating the diverse levels of influence among entities (users/items/social connections) is very important in advancing social recommender systems. Towards this goal, we propose a new model named Social-Trust-Aware Variational Recommendation (SOAP-VAE). Particularly, SOAP-VAE leverages graph attention network techniques to capture the varying levels of influence and the complex interaction patterns among all the entities collectively and holistically. In doing so, heterogeneity among entities is obtained seamlessly. Consequently, we generate social-trust-aware item embedding representations in which the right level of influence has been integrated. Next, based on these rich social-trust-aware item representations, we formulate the first-ever social-trust-aware prior in literature. Unlike priors utilized in earlier VAE-based recommendation models, this novel prior aids in dealing with the issue of posterior-collapse and can effectively capture the uncertainty of latent space. In effect, the model produces better latent representations, which significantly alleviates the data sparsity issue. Finally, we empirically show that SOAP-VAE outperforms several state-of-the-art baselines on three real-world data sets.