In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new Si gn-aware Recommender system based on GNN models. Specifically, SiReN has three key components: 1) constructing a signed bipartite graph for more precisely representing users' preferences, which is split into two edge-disjoint graphs with positive and negative edges each; 2) generating two embeddings for the partitioned graphs with positive and negative edges via a GNN model and a multilayer perceptron (MLP), respectively, and then using an attention model to obtain the final embeddings; and 3) establishing a sign-aware Bayesian personalized ranking (BPR) loss function in the process of optimization. Through comprehensive experiments, we empirically demonstrate that SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.

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.

In social network analysis, bridges play a critical role in maintaining connectivity and facilitating the dissemination of information between communities. Despite increasing interest in bridge structures, a systematic classification of their roles across various network types remains unexplored. This study introduces a categorization of bridges into structural and functional types. Structural bridges maintain connectivity by preventing network fragmentation, whereas functional bridges facilitate the flow of information between communities. We conducted a comprehensive literature review and classified existing studies within this framework. The findings clarify the distinct roles of bridges and provide valuable insight for devising effective strategies for network design and analysis.

Visual Question Answering (VQA) is one of the important tasks that can help artificial intelligence understand the real world. Recently, with the growing popularity of zero-shot VQA, research has focused on utilizing external knowledge to tackle complex problems, especially those requiring common sense. However, existing studies that attempt to leverage external knowledge often use large amounts of knowledge without any selection process. Since some of this knowledge may not contribute to accurate predictions, the process of selecting relevant knowledge is essential. To address this issue, we propose Diverse Knowledge Selection for Enhanced Visual Question Answering (DKSVQA), which consists of three stages: Image-Context Generation, Similarity-based Knowledge Selection, and Query-Knowledge Graph-based Knowledge Selection. We demonstrate the superior performance of DKSVQA on two VQA benchmark datasets and compare it with zero-shot VQA baseline models. We highlight both the effectiveness and efficiency of DKSVQA through extensive experiments. For reproducibility, the source code is available at https://github.com/gooriiie/DKSVQA.

In this work, we propose a novel motor disorder diagnosis model based on graph neural networks (GNNs). This model maximizes model performance by incorporating advanced preprocessing techniques such as Fast Fourier Transform (FFT) and Wavelet Transform (WT). Conventional machine learning and deep learning models such as CNN and SVM find it difficult to handle nonlinear high-dimensional data in motor disorder diagnosis. On the other hand, GNN effectively handles these complex data structures, enabling more accurate and reliable defect classification. Experimental results show that the GNN-based model combining FFT and WT performed well in the diagnosis of motor disorder. Specifically, the FFT-based GNN showed high accuracy, accuracy, and reproducibility at an F1 score of 0.95. The GNN model has lower misclassification rate and higher reliability compared to other models, and ran consistently for various defect types. This is because GNNs can capture complex relationships within frequency domain function (FFT) and time frequency domain pattern (WT). For example, rotational imbalance defects are accurately classified thanks to the ability of GNNs to model harmonic frequency relationships, and bearing defects are accurately classified thanks to the model sensitivity to local frequency spikes that are effectively represented on nodes and edges of the graph. These results suggest that GNN-based motor defect diagnostic systems not only improve diagnostic accuracy, but also have significant potential for real-time applications in manufacturing environments. The system is expected to reduce maintenance costs and improve operational efficiency. The proposed GNN model makes an important contribution by providing practical solutions for the detection and prevention of motion disorders.