Linear Sequential Recommendation Models using Textual Side Information

Dongcheol Lee, Minjin Choi, Jongwuk Lee

http://doi.org/10.5626/JOK.2025.52.6.529

Recently, research on leveraging auxiliary information in sequential recommendation systems is being actively conducted. Most approaches have focused on combining language models with deep neural networks. However, they often lead to high computational costs and latency issues. While linear recommendation models can serve as an efficient alternative, research on how to effectively incorporate auxiliary information is lacking. This study proposed a framework that could effectively utilize auxiliary information within a linear model. Since textual data cannot be directly used in linear model training, we transformed item texts into dense vectors using a pre-trained text encoder. Although these vectors contained rich information, they failed to capture relationships between items. To address this, we applied graph convolution to obtain enhanced item representations. These representations were then used alongside the user-item interaction matrix for linear model training. Extensive experiments showed that the proposed method improved the overall performance, particularly in recommending less popular items.

Protein-Ligand Binding Affinity Prediction Using Protein Modality Alignme

Seungyong Lee, Sanghyun Park

http://doi.org/10.5626/JOK.2025.52.5.415

Identifying molecules with high binding affinity to a target protein for drug candidate discovery requires significant resources and time. Deep learning-based protein-ligand binding affinity prediction research plays a crucial role in addressing this challenge. Existing studies have utilized protein sequence and structural information along with ligand 2D structures. However, they have limitations in fully capturing complex interactions. Additionally, while sequence, structure, and surface information are used for protein modeling, previous approaches have struggled to incorporate their dependent relationships into the model. In this paper, we proposed a model that could inject these dependencies by aligning protein sequence, structure, and surface information based on sequence data. Furthermore, our model leverages both 2D structure of the ligand and its 3D representation using an SE(3)-invariant graph neural network. The proposed model outperformed existing baseline models. An ablation study demonstrated the importance of aligning different protein modalities and incorporating both 2D and 3D ligand information.

Enhancing Molecular Understanding in LLMs through Multimodal Graph-SMILES Representations

http://doi.org/10.5626/JOK.2025.52.5.379

Recent advancements in large language models (LLMs) have shown remarkable performace across various tasks, with increasing focus on multimodal research. Notably, BLIP-2 can enhance performance by efficiently aligning images and text using a Q-Former, aided by an image encoder pre-trained on multimodal data. Inspired by this, the MolCA model extends BLIP-2 to the molecular domain to improve performance. However, the graph encoder in MolCA is pre-trained on unimodal data, necessitating updates during model training, which is a limitation. Therefore, this paper replaced it with a graph encoder pre-trained on multimodal data and frozen while training the model. Experimental results showed that using the graph encoder pre-trained on multimodal data generally enhanced performance. Additionally, unlike the graph encoder pre-trained on unimodal data, which performed better when updated, the graph encoder pre-trained on multimodal data achieved superior results across all metrics when frozen.

Root Cause Analysis for Microservice Systems Using Anomaly Propagation by Resource Sharing

Junho Park, Joyce Jiyoung Whang

http://doi.org/10.5626/JOK.2025.52.4.341

Identifying root causes of failures in microservice systems remains a critical challenge due to intricate interactions among resources and propagation of errors. We propose AnoProp, a novel model for root cause analysis to address challenges by capturing inter-resource interactions and the resulting propagation of anomalies. AnoProp incorporates two core techniques: the anomaly score measurement for metrics using regression models and the root cause score evaluation for resources based on the propagation rate of these anomalies. Experimental results using an Online Boutique dataset demonstrated that AnoProp surpassed existing models across various evaluation metrics, validating its ability to provide balanced performance for different types of root causes. This study underscores the potential of AnoProp to enhance system stability and boost operational efficiency in microservice environments.

Geographical Adaptive Attention Model for Points of Interest Recommendation

Muyeon Jo, Sejin Chun, Jungkyu Han

http://doi.org/10.5626/JOK.2025.52.3.217

Geographical influence, stemming from the location of Points of Interest (POIs), plays a vital role in POI recommendation. Most current studies utilize geographical information such as distance and location to define and extract POI-specific geographical influences for personalized recommendations. These approaches primarily emphasize distance-based influence, which gauges user preferences based on proximity, while often overlooking area-based influence, which reflects preferences for regions with specific POI characteristics. This paper introduces a POI recommendation model based on an attention network that integrates both distance- and area-based influences. The model adaptively assesses how previously visited POIs impact the likelihood of visiting a target POI, taking into account regional characteristics and user preferences. Experiments conducted on real-world datasets indicate that the proposed method significantly outperforms baseline models, achieving improvements of approximately 6–12% in Prec@10, 8–10% in Recall@10, and 6–7% in HR@10.

Drug Toxicity Prediction Using Integrated Graph Neural Networks and Attention-Based Random Walk Algorithm

Jong-Hoon Park, Jae-Woo Chu, Young-Rae Cho

http://doi.org/10.5626/JOK.2025.52.3.234

The traditional drug development process is often burdened by high costs and lengthy timelines, leading to increasing interest in AI-based drug development. In particular, the importance of AI models for preemptively evaluating drug toxicity is being emphasized. In this study, we propose a novel drug toxicity prediction model, named Integrated GNNs and Attention Randon Walk (IG-ARW). The proposed method integrates various Graph Neural Network (GNN) models and uses attention mechanisms to compute random walk transition probabilities, extracting graph features precisely. The model then conducts random walks to extract node features and graph features, ultimately predicting drug toxicity. IG-ARW was evaluated on three different datasets, demonstrating strong performances with AUC scores of 0.8315, 0.8894, and 0.7476, respectively. Notably, the model was proven to be highly effective not only in toxicity prediction, but also in predicting other drug characteristics.

HAGCN: Heterogeneous Attentive GCN for Gene-Disease Association

Kibeom Kim, Giltae Song

http://doi.org/10.5626/JOK.2025.52.2.161

Predicting gene-disease associations (GDAs) is essential for understanding molecular mechanisms, diagnosing disease, and targeting genes. Validating causal relationships between diseases and genes using experimental methods can be extremely costly and time-consuming. Deep learning, particularly graph neural networks, has shown great promise in this area. However, most models rely on single-source, homogeneous graphs. Another is the need for expert knowledge in manual definition of meta-paths to build multi-source heterogeneous graphs. Recognizing these challenges, the present study introduces the Heterogeneous Attentive Graph Convolution Network (HAGCN). HAGCN processes heterogeneous biological entity association graphs as input. We construct the input graphs using the biological association information from curated databases such as Gene Ontology, Disease Ontology, Human Phenotype Ontology, and TBGA. HAGCN learns the relationship heterogeneity between biological entities without meta-paths by using the attention mechanism. HAGCN achieved the best performance in AUC-ROC in a binary classification task to predict gene-disease association, and also achieved competitive performance in F1 score, MCC, and accuracy against baselines. We believe that HAGCN can accelerate the discovery of disease-associated genes and

An Effective Graph Edit Distance Model Using Node Mapping Information

Jun-Gyu Lee, Jongik Kim

http://doi.org/10.5626/JOK.2025.52.1.88

Graph Edit Distance (GED) is the most representative method for quantifying similarity between graphs. However, calculating an exact GED is an NP-Hard problem, which incurs a prohibitively large amount of computational cost. To efficiently compute GED, recent studies have focused on deriving an approximate GED between graphs using deep learning models. However, existing models tend to exhibit large approximation errors and suffer from insufficient interpretability because they do not consider node-to-node relationships between graphs. To remedy these problems faced by existing models, a model that could learn a mapping matrix through node-level embeddings of two graphs was proposed in this study to provide better interpretability of the GED approximation while minimizing information loss during the learning process. Results of experiments showed that the proposed model consistently outperformed existing models.