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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Journal ArticleDOI
A Comprehensive Survey on Graph Neural Networks
TL;DR: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields and proposes a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNS, convolutional GNN’s, graph autoencoders, and spatial–temporal Gnns.
Journal ArticleDOI
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
TL;DR: A comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research.
Proceedings Article
DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding
TL;DR: A novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise) and a light-weight neural net is proposed to learn sentence embedding, based solely on the proposed attention without any RNN/CNN structure.
Proceedings ArticleDOI
Adversarially regularized graph autoencoder for graph embedding
TL;DR: A novel adversarial graph embedding framework for graph data that encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure.
Proceedings ArticleDOI
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
TL;DR: This paper proposes a general graph neural network framework designed specifically for multivariate time series data that outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.