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.
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Journal ArticleDOI
CFOND: Consensus Factorization for Co-Clustering Networked Data
TL;DR: CFOND, a consensus factorization based framework for co-clustering networked data, argues that feature values and linkages provide useful information from different perspectives, but they are not always consistent and therefore need to be carefully aligned for best clustering results.
Proceedings ArticleDOI
DAGCN: Dual Attention Graph Convolutional Networks
TL;DR: DAGCN automatically learns the importance of neighbors at different hops using a novel attention graph convolution layer, and then employs a second attention component, a self-attention pooling layer, to generalize the graph representation from the various aspects of a matrix graph embedding.
Journal ArticleDOI
Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network
TL;DR: A novel probabilistic topic model to automatically recommend citations for researchers is proposed, which considers not only text content similarity between papers but also community relevance among authors for effective citation recommendation.
Journal ArticleDOI
One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting
TL;DR: The experiments on the common NAS search space demonstrate that NSAS and it variants improve the predictive ability of supernet training in one-shot NAS with remarkable and efficient performance on the CIFAR-10, CIFar-100, and ImageNet datasets.
Journal ArticleDOI
Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning
TL;DR: The experimental results demonstrate the effectiveness of the proposed deep residual network method in lung nodule classification for CT images, which improved the accuracy and the false positive rate compared with the conventional support vector machine (SVM)-based CAD system.