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cs.LG2026
UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
Hyunju Kang, Geonhee Han, Hogun Park
Node representation learning, such as Graph Neural Networks (GNNs), has emerged as a pivotal method in machine learning. The demand for reliable explanation generation surges, yet…
cs.LG2025
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition
Heesoo Jung, Hogun Park
Self-supervised learning (SSL) in graphs has garnered significant attention, particularly in employing Graph Neural Networks (GNNs) with pretext tasks initially designed for other…
cs.LG2025
CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders
Jongwon Park, Heesoo Jung, Hogun Park
Recent Self-Supervised Learning (SSL) methods encapsulating relational information via masking in Graph Neural Networks (GNNs) have shown promising performance. However, most exist…