C
Chengqi Zhang
Researcher at University of Technology, Sydney
Publications - 495
Citations - 20605
Chengqi Zhang is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Association rule learning & Knowledge extraction. The author has an hindex of 54, co-authored 478 publications receiving 14519 citations. Previous affiliations of Chengqi Zhang include Information Technology University & Beihang University.
Papers
More filters
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.
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.
Posted Content
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
TL;DR: Wang et al. as discussed by the authors proposed a novel graph neural network architecture, Graph WaveNet, for spatial-temporal graph modeling, which can precisely capture the hidden spatial dependency in the data.