S
Shirui Pan
Researcher at Monash University
Publications - 187
Citations - 14539
Shirui Pan is an academic researcher from Monash University. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 36, co-authored 151 publications receiving 7202 citations. Previous affiliations of Shirui Pan include University of Technology, Sydney & Northwest A&F University.
Papers
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Proceedings ArticleDOI
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
TL;DR: Li et al. as mentioned in this paper proposed a self-supervised approach to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning, where they first generate two augmented views from the input graph based on local and global perspectives.
Book ChapterDOI
Classifier ensemble for uncertain data stream classification
TL;DR: This paper proposes two types of ensemble based algorithms, Static Classifier Ensemble (SCE) and Dynamic Classifier ensemble (DCE) for mining uncertain data streams and experimental results reveal that DCE algorithm outperforms SCE algorithm.
Proceedings ArticleDOI
OpenWGL: Open-World Graph Learning
Man Wu,Shirui Pan,Xingquan Zhu +2 more
TL;DR: In this paper, an open-world graph learning paradigm is proposed, where the learning goal is to not only classify nodes belonging to seen classes into correct groups, but also classify nodes not belonging to existing classes to an unseen class.
Proceedings Article
Graph Stochastic Neural Networks for Semi-supervised Learning
TL;DR: A learnable graph neural network coupled with a high-dimensional latent variable to model the distribution of the classification function, and further adopt the amortised variational inference to approximate the intractable joint posterior for missing labels and the latent variable is introduced.
Journal ArticleDOI
Identify Topic Relations in Scientific Literature Using Topic Modeling
TL;DR: A systematic methodology for topic analysis in scientific literature corpora to face the concerns of conducting post topic modeling analysis is proposed and a topic relation identification approach is presented to quantitatively model the relations among the topics.