Discrete Network Embedding
Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The network embedding is typically represented in c...
Publications
6 shownBinarized attributed network embedding
Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding models are designed in continuous Euclidea...
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spati...
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assump...
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research ...
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively. Attention mechanisms ...
Frequent Co-Authors
Researcher Info
- h-index
- 6
- Publications
- 6
- Citations
- 7,021
- Institution
- University of Technology Sydney
External Links
Identifiers
- ORCID
- 0000-0003-0794-527X
Impact Metrics
h-index
6
h-index: Number of publications with at least h citations each.