May 30, 2024 � This study explores the potential of deep graph learning to achieve a better trade-off between the quality and efficiency of K-agnostic CD.
Aug 24, 2024 � Experiments on public datasets with various scales demonstrate that PRoCD can ensure higher efficiency in K-agnostic CD without significant�...
This study explores the potential of deep graph learning to achieve a better trade-off between the quality and efficiency of K K K italic_K -agnostic CD, where�...
Aug 25, 2024 � This study explores the potential of deep graph learning to achieve a better trade-off between the quality and efficiency of K-agnostic. CD,�...
Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation. Meng Qin (Department of CSE, HKUST); Chaorui�...
Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation, KDD 2024,. with Meng Qin, Chaorui Zhang, Weixi�...
Read more. Download � Share � Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation � Conference Paper.
Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation. KDD 2024: 2467-2478. [i4]. view. electronic�...
Read more. Share � Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation � Conference Paper. August 2024.
尽管许多CD 方法的提出都具有令人印象深刻的质量或效率，但平衡这两个方面仍然是一个挑战。本研究探讨了深度图学习的潜力，以实现K 不可知CD 的质量和效率之间的更好权衡，�...