1 code implementation • 19 Feb 2024 • Yongquan He, Peng Zhang, Luchen Liu, Qi Liang, Wenyuan Zhang, Chuang Zhang
In recent years, temporal knowledge graph (TKG) reasoning has received significant attention.
1 code implementation • 9 Sep 2023 • Si-Yu Yi, Wei Ju, Yifang Qin, Xiao Luo, Luchen Liu, Yong-Dao Zhou, Ming Zhang
Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years.
no code implementations • 31 Aug 2023 • Siyu Yi, Zhengyang Mao, Wei Ju, Yongdao Zhou, Luchen Liu, Xiao Luo, Ming Zhang
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution.
1 code implementation • 25 May 2021 • Xiao Luo, Daqing Wu, Yiyang Gu, Chong Chen, Luchen Liu, Jinwen Ma, Ming Zhang, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua
Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior.
1 code implementation • 10 Dec 2020 • Jiangxia Cao, Xixun Lin, Shu Guo, Luchen Liu, Tingwen Liu, Bin Wang
However, the global properties of bipartite graph, including community structures of homogeneous nodes and long-range dependencies of heterogeneous nodes, are not well preserved.