no code implementations • 27 May 2023 • Hao Geng, Deqing Wang, Fuzhen Zhuang, Xuehua Ming, Chenguang Du, Ting Jiang, Haolong Guo, Rui Liu
To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.
1 code implementation • 20 Nov 2022 • Ziming Wan, Deqing Wang, Xuehua Ming, Fuzhen Zhuang, Chenguang Du, Ting Jiang, Zhengyang Zhao
To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning.