47 citations · 151 across the 32 of their papers we have counts for
9 papers · 1 filter
SHGNN: Structure-Aware Heterogeneous Graph Neural Network
Wentao Xu, Yingce Xia, Weiqing Liu +3
Many real-world graphs (networks) are heterogeneous with different types of nodes and edges. Heterogeneous graph embedding, aiming at learning the low-dimensional node representati…
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
Zhiping Luo, Wentao Xu, Weiqing Liu +3
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering…
HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information
Wentao Xu, Weiqing Liu, Lewen Wang +4
Stock trend forecasting, which forecasts stock prices' future trends, plays an essential role in investment. The stocks in a market can share information so that their stock prices…
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
Wentao Xu, Weiqing Liu, Jiang Bian +2
The multivariate time series forecasting has attracted more and more attention because of its vital role in different fields in the real world, such as finance, traffic, and weathe…
Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation
Hengxu Lin, Dong Zhou, Weiqing Liu +1
Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance. Within the modern portfolio construction framework…
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
Hengxu Lin, Dong Zhou, Weiqing Liu +1
Successful quantitative investment usually relies on precise predictions of the future movement of the stock price. Recently, machine learning based solutions have shown their capa…