Zobrazeno 1 - 10 of 19 pro vyhledávání: '"Xunkai Li"'
1
Akademický článek
Siamese Network Based Multiscale Self-Supervised Heterogeneous Graph Representation Learning
Autor: Zijun Chen, Lihui Luo, Xunkai Li, Bin Jiang, Qiang Guo, Chunpeng Wang
Publikováno v: IEEE Access, Vol 10, Pp 98490-98500 (2022)
Owing to label-free modeling of complex heterogeneity, self-supervised heterogeneous graph representation learning (SS-HGRL) has been widely studied in recent years. The goal of SS-HGRL is to design an unsupervised learning framework to represent com
Externí odkaz: https://doaj.org/article/5083094c38264f7faad87943c47445be
Zobrazit plný text záznamu
2
Akademický článek
A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering
Autor: Ronghui Guo, Xunkai Li, Youpeng Hu, Yixuan Wu, Xin Xiong, Meixia Qu
Publikováno v: IEEE Access, Vol 9, Pp 77407-77415 (2021)
Recently, recommender systems based on Graph Convolution Network (GCN) have become a research hotspot, especially in collaborative filtering. However, most GCN-based models have inferior embedding propagation mechanism, leading to low information ext
Externí odkaz: https://doaj.org/article/93654cbef37a493e8efd4d1108d02479
Zobrazit plný text záznamu
3
Akademický článek
A Deep Graph Structured Clustering Network
Autor: Xunkai Li, Youpeng Hu, Yaoqi Sun, Ji Hu, Jiyong Zhang, Meixia Qu
Publikováno v: IEEE Access, Vol 8, Pp 161727-161738 (2020)
Graph clustering is a fundamental task in data analysis and has attracted considerable attention in recommendation systems, mapping knowledge domain, and biological science. Because graph convolution is very effective in combining the feature informa
Externí odkaz: https://doaj.org/article/80709615459f42f7b98c8760cdd85f31
Zobrazit plný text záznamu
6
Akademický článek
A Scalable Deep Network for Graph Clustering via Personalized PageRank
Autor: Yulin Zhao, Xunkai Li, Yinlin Zhu, Jin Li, Shuo Wang, Bin Jiang
Publikováno v: Applied Sciences, Vol 12, Iss 11, p 5502 (2022)
Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations:
Externí odkaz: https://doaj.org/article/6872272a395c482abc47c38cdae2c38f
Zobrazit plný text záznamu
9
A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering
Autor: Yixuan Wu, Ronghui Guo, Youpeng Hu, Xunkai Li, Xin Xiong, Meixia Qu
Publikováno v: IEEE Access, Vol 9, Pp 77407-77415 (2021)
Recently, recommender systems based on Graph Convolution Network (GCN) have become a research hotspot, especially in collaborative filtering. However, most GCN-based models have inferior embedding propagation mechanism, leading to low information ext
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7275e145e4d7bf298eb4425d43d118e
https://doi.org/10.1109/access.2021.3083600
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje