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
Time series feature learning with labeled and unlabeled data
TL;DR: A new Semi-Supervised Shapelets Learning model is presented to efficiently learn shapelets by using both labeled and unlabeled time series data in an integrated model that considers the least squares regression, the power of the pseudo-labels, shapelets regularization, and spectral analysis.
Proceedings ArticleDOI
Binarized attributed network embedding
TL;DR: A new Weisfeiler-Lehman proximity matrix is defined to capture data dependence between node links and attributes by aggregating the information of node attributes and links from neighboring nodes to a given target node in a layer-wise manner.
Proceedings ArticleDOI
Unsupervised Domain Adaptive Graph Convolutional Networks
TL;DR: A novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs, which jointly exploits local and global consistency for feature aggregation and facilitates knowledge transfer between graphs.
Journal ArticleDOI
Self-adaptive attribute weighting for Naive Bayes classification
TL;DR: The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way.
Journal ArticleDOI
Advances in processing, mining, and learning complex data: from foundations to real-world applications
TL;DR: This special issue contributes to the fundamental research in processing, mining, and learning complex data, focusing on the analysis of complex data sources.