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- Centre for Quantum Computation and Communication TechnologyAU38 papers
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10 papers · 1 filter
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
Shiyuan Li, Yixin Liu, Qingfeng Chen +2
Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs), has received increasing attention owing to its efficacy in handling graph-structured data.…
Domain-adaptive Message Passing Graph Neural Network
Xiao Shen, Shirui Pan, Kup-Sze Choi +1
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels,…
Learning Strong Graph Neural Networks with Weak Information
Yixin Liu, Kaize Ding, Jianling Wang +3
Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input graph data suf…
Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs
Linhao Luo, Reza Haffari, Shirui Pan
Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks (DGNNs) typically require a significant amount of…
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
Yixin Liu, Kaize Ding, Huan Liu +1
Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training da…
Spectrum-Guided Adversarial Disparity Learning
Zhe Liu, Lina Yao, Lei Bai +2
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between…