57 citations · 184 across the 33 of their papers we have counts for
5 papers · 2 filters
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
Yizhen Zheng, Ming Jin, Shirui Pan +4
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information,…
Unsupervised Domain-adaptive Hash for Networks
Tao He, Lianli Gao, Jingkuan Song +1
Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms. At the same time, labels may only be avail…
Semi-supervised Network Embedding with Differentiable Deep Quantisation
Tao He, Lianli Gao, Jingkuan Song +1
Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks. For large networks, the trained embeddings o…
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Ming Jin, Yizhen Zheng, Yuan-Fang Li +3
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To…
Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction
Bhagya Hettige, Weiqing Wang, Yuan-Fang Li +2
Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide gran…