Community detection in signed networks is challenging due to the presence of both positive and negative edges, which violate the homophily assumption commonly used in traditional methods. In this paper, we present CR-SGCN, an unsupervised framework for community detection in signed networks. It combines a signed GCN encoder, a soft community assignment layer, and a degree-corrected stochastic block model decoder. To enhance boundary separation, we introduce an edge-level signed conductance regularization that pulls intra-community embeddings closer and pushes inter-community ones apart. Without requiring labels, CR-SGCN effectively captures community structure even under edge sparsity. Experiments on real-world signed networks show consistent gains in signed modularity and structural separation over existing baselines. The results demonstrate the robustness and effectiveness of CR-SGCN for unsupervised signed community detection.

We present five approaches for interpolating missing data in real-world traffic datasets using basic, statistical, and generative model methods. The effectiveness of these approaches is evaluated using graph deep learning models, and the results show that imputing missing data improves the performance of traffic prediction models, especially when dealing with a higher proportion of missing data. The experiments are conducted on two real-world datasets, METR-LA and PEMS-BAY, to evaluate the proposed methods comprehensively. The results demonstrate that imputing missing data significantly enhances the performance of traffic prediction models, particularly when dealing with a higher proportion of missing data.

Identifying prognostic genes associated with patient survival is an important goal in cancer genomics, as this information could inform treatment approaches and improve patient outcomes. However, the identification of prognostic genes is complicated by the high dimensionality of genetic data, which makes their identification computationally intensive. Furthermore, most cancer genomics studies lack appropriate low-risk groups against which to compare. To address these issues, we present a framework that identifies candidate prognostic genes by integrating representation learning and statistical analysis approaches. Specifically, we propose a collaborative filtering-derived mechanism to represent patients in order of their survival risk, facilitating their dichotomization. We also propose a mechanism that allows embedded gene vectors to be polarized on the extremities of, or centered on, both reference axes to facilitate recommendations. Restricting our analysis to a few representative genes within each cluster allowed for the efficient identification of prognostic genes. Finally, we demonstrate the potential of this proposed framework for identifying prognostic genes.