GUG 2nd seminar
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25년 9월 4주차 그래프 오마카세
Are Large Language Models Good Temporal Graph Learners? Youtube link : https://www.youtube.com/watch?v=jmCwOQX9Ank * 이번 주 오마카세는 TGL (Temporal Graph Learning) 세미나에서 대규모 언어 모델(LLM)이 관계형 데이터베이스에서 어떻게 딥러닝을 수행하는지에 초점을 맞추어 발표된, 기존의 시계열 그래프 학습 방법론에 LLM을 접목하여 새로운 가능성을 탐색한 '대규모
25년 9월 3주차 그래프 오마카세
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned ExpertsGraph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts. However, it remains unclear how to build machine learning architectures that generalize to
25년 9월 2주차 그래프 오마카세
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementLong-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks,
25년 9월 1주차 그래프 오마카세
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning DatasetsBenchmark datasets have proved pivotal to the success of graph learning, and good benchmark datasets are crucial to guide the development of the field. Recent research has