| Abstract: |
| In real-world scenarios, many data types are naturally represented as graphs with complex topological structures, inherently reflecting real-life systems. In recent years, Graph Neural Networks (GNNs) have attracted widespread attention as powerful tools for processing graph-structured data, with applications spanning diverse domains. This report examines several core issues in applying graph neural networks. First, we address stock movement prediction in the fintech domain. This task demands handling the complexity and dynamic nature of financial markets, making it a representative challenge in financial time series analysis. To tackle this issue, we propose a novel model: the Time-Lag and Edge Feature-Incorporated Graph Attention Network (TILE-GAT).
Second, traffic flow prediction is a critical task in intelligent transportation systems, where the central challenge lies in capturing complex spati-temporal dependencies within road networks. In particular, modeling spatial dependencies requires accurately and efficiently characterizing traffic flow transmission between nodes, especially the causal relationships among them. To address this limitation, we introduce a Spatio-Temporal Causal Graph Neural Network (STCGNN) framework for traffic flow prediction. |
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