8 citations · 9 across the 10 of their papers we have counts for
8 papers · 1 filter
PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories
Hyunsung Kim, Kunhee Lee, Sangwoo Seo +3
Despite recent advances in AI, event data collection in soccer still relies heavily on labor-intensive manual annotation. Although prior work has explored automatic event detection…
Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks
Hyunsung Kim, Sangwoo Seo, Hoyoung Choi +3
Evaluating defensive performance in soccer remains challenging, as effective defending is often expressed not through visible on-ball actions such as interceptions and tackles, but…
Adaptive Graph Rewiring to Mitigate Over-Squashing in Mesh-Based GNNs for Fluid Dynamics Simulations
Sangwoo Seo, Hyunsung Kim, Jiwan Kim +1
Mesh-based simulation using Graph Neural Networks (GNNs) has been recognized as a promising approach for modeling fluid dynamics. However, the mesh refinement techniques which allo…
Disentangling Hyperedges through the Lens of Category Theory
Yoonho Lee, Junseok Lee, Sangwoo Seo +3
Despite the promising results of disentangled representation learning in discovering latent patterns in graph-structured data, few studies have explored disentanglement for hypergr…
Target Circuit Matching in Large-Scale Netlists using GNN-Based Region Prediction
Sangwoo Seo, Jimin Seo, Yoonho Lee +4
Subgraph matching plays an important role in electronic design automation (EDA) and circuit verification. Traditional rule-based methods have limitations in generalizing to arbitra…
Global Context-aware Representation Learning for Spatially Resolved Transcriptomics
Yunhak Oh, Junseok Lee, Yeongmin Kim +3
Spatially Resolved Transcriptomics (SRT) is a cutting-edge technique that captures the spatial context of cells within tissues, enabling the study of complex biological networks. R…
Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck
Sangwoo Seo, Sungwon Kim, Jihyeong Jung +2
Temporal Graph Neural Networks (TGNN) have the ability to capture both the graph topology and dynamic dependencies of interactions within a graph over time. There has been a growin…
Interpretable Prototype-based Graph Information Bottleneck
Sangwoo Seo, Sungwon Kim, Chanyoung Park
The success of Graph Neural Networks (GNNs) has led to a need for understanding their decision-making process and providing explanations for their predictions, which has given rise…