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Junseok Lee

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Disentangling Hyperedges through the Lens of Category Theory

Yoonho Lee
Junseok Lee
Sangwoo Seo
Sungwon Kim
Yeongmin Kim
Chanyoung Park

Despite the promising results of disentangled representation learning in discovering latent patterns in graph-structured data, few studies have explored disentanglement for hypergraph-structured data. Integrating hyperedge disentanglement into hypergraph neural networks enables models to leverage hidden hyperedge semantics, such as unannotated relations between nodes, that are associated with labels. This paper presents an analysis of hyperedge disentanglement from a category-theoretical perspective and proposes a novel criterion for disentanglement derived from the naturality condition. Our proof-of-concept model experimentally showed the potential of the proposed criterion by successfully capturing functional relations of genes (nodes) in genetic pathways (hyperedges).

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AAAI Conference 2023 Conference Paper

Heterogeneous Graph Learning for Multi-Modal Medical Data Analysis

Sein Kim
Namkyeong Lee
Junseok Lee
Dongmin Hyun
Chanyoung Park

Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complementary perspectives on the same patient, resulting in more accurate clinical decisions when they are properly combined. However, despite its significance, how to effectively fuse the multi-modal medical data into a unified framework has received relatively little attention. In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data. Specifically, we construct a multiplex network that incorporates multiple types of non-image features of patients to capture the complex relationship between patients in a systematic way, which leads to more accurate clinical decisions. Extensive experiments on various real-world datasets demonstrate the superiority and practicality of HetMed. The source code for HetMed is available at https://github.com/Sein-Kim/Multimodal-Medical.

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AAAI Conference 2022 Conference Paper

Augmentation-Free Self-Supervised Learning on Graphs

Namkyeong Lee
Junseok Lee
Chanyoung Park

Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods. However, we argue that without carefully designed augmentation techniques, augmentations on graphs may behave arbitrarily in that the underlying semantics of graphs can drastically change. As a consequence, the performance of existing augmentationbased methods is highly dependent on the choice of augmentation scheme, i. e. , hyperparameters associated with augmentations. In this paper, we propose a novel augmentation-free self-supervised learning framework for graphs, named AF- GRL. Specifically, we generate an alternative view of a graph by discovering nodes that share the local structural information and the global semantics with the graph. Extensive experiments towards various node-level tasks, i. e. , node classification, clustering, and similarity search on various realworld datasets demonstrate the superiority of AFGRL. The source code for AFGRL is available at https: //github. com/ Namkyeong/AFGRL.

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IROS Conference 2020 Conference Paper

Development and Analysis of Digging and Soil Removing Mechanisms for Mole-Bot: Bio-Inspired Mole-Like Drilling Robot

Junseok Lee
Christian Tirtawardhana
Hyun Myung

Interests in exploration of new energy resources are increasing due to the exhaustion of existing resources. To explore new energy sources, various studies have been conducted to improve the drilling performance of drilling equipment for deep and strong ground. However, with better performance, the modern drilling equipment is bulky and, furthermore, has become inconvenient in both installation and operation, for it takes complex procedures for complex terrains. Moreover, environmental issues are also a concern because of the excessive use of mud and slurry to remove excavated soil. To overcome these limitations, a mechanism that combines an expandable drill bit and link structure to simulate the function of the teeth and forelimbs of a mole is proposed. In this paper, the proposed expandable drill bit simplifies the complexity and high number of degrees of freedom of the animal head. In addition, a debris removal mechanism mimicking a shoulder structure and forefoot movement is proposed. For efficient debris removal, the proposed mechanism enables the simultaneous rotation and expanding/folding motions of the drill bit by using a single actuator. The performance of the proposed system is evaluated by dynamic simulations and experiments.

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IROS Conference 2017 Conference Paper

Automatic page-turning mechanism with near-field electroadhesive force for linearly correctable imaging

Junseok Lee
Wonseok Jeon
Youngsu Cha
Hyunseok Yang

Recently in tandem with the spread of portable devices for reading electronic books, devices for digitizing paper books, called book scanners, are developed to meet the increased demand for digitizing privately owned books. However, conventional book scanners still have complex components to mechanically turn pages and to rectify the acquired images that are inevitably distorted by the curvy book surface. Here, we present the multi-scale mechanism that turns pages electronically using electroadhesive force generated by a micro-scale structure. Its another advantage is that perspective correction of image processing is applicable to readily reconstruct the distorted images of pages. Specifically, to turn one page at a time not two pages, we employ a micro-scale structure to generate near-field electroadhesive force that decays rapidly and accordingly attracts objects within tens of micrometers. We analyze geometrical parameters of the micro-scale structure to improve the decay characteristics. We find that the decay characteristics of electroadhesive force definitely depends upon the geometrical period of the micro-scale structure, while its magnitude depends on a variety of parameters. Based on this observation, we propose a novel electrode configuration with improved decay characteristics. Dynamical stability and kinematic requirements are also examined to successfully introduce near-field electroadhesive force into our digitizing process.

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