5 papers
ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs
Xinghe Cheng, Jiapu Wang, Chaobo He +2
Prerequisite relation learning is central to adaptive instruction, yet existing methods often formulate it as conventional link prediction, limiting their ability to adaptively int…
Relational Probing: LM-to-Graph Adaptation for Financial Prediction
Yingjie Niu, Changhong Jin, Rian Dolphin +1
Language models can be used to identify relationships between financial entities in text. However, while structured output mechanisms exist, prompting-based pipelines still incur a…
GAPNet: Plug-in Jointly Learning Task-Specific Graph for Dynamic Stock Relation
Yingjie Niu, Lanxin Lu, Changhong Jin +1
The advent of the web has led to a paradigm shift in the financial relations, with the real-time dissemination of news, social discourse, and financial filings contributing signifi…
NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction
Yingjie Niu, Mingchuan Zhao, Valerio Poti +1
Graph representation learning methods have been widely adopted in financial applications to enhance company representations by leveraging inter-firm relationships. However, current…
Evaluating Financial Relational Graphs: Interpretation Before Prediction
Yingjie Niu, Lanxin Lu, Rian Dolphin +2
Accurate and robust stock trend forecasting has been a crucial and challenging task, as stock price changes are influenced by multiple factors. Graph neural network-based methods h…