1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2021
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Langzhang Liang, Cuiyun Gao, Shiyi Chen +5
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, l…
cs.LG2021★ 1 cited
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning
Shiyi Chen, Ziao Wang, Xinni Zhang +2
Graph representation learning has long been an important yet challenging task for various real-world applications. However, their downstream tasks are mainly performed in the setti…
cs.LG2019
Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better
Yaping Zheng, Shiyi Chen, Xinni Zhang +3
Community detection has long been an important yet challenging task to analyze complex networks with a focus on detecting topological structures of graph data. Essentially, real-wo…