19 papers
TradingMoE: Routing the Right Experts in Evolving Markets
Chang Zhou, Xingtong Yu, Minbin Huang +4
Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can…
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Quanxin Wang, Xuanting Xie, Bingheng Li +4
Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a…
HyperClaim: Fine-Grained Cross-Modal Hypergraph Reasoning for Video Misinformation Detection
Xiangbo Wang, Jiasheng Zhang, Xingtong Yu +2
The paper introduces HyperClaim, a temporal hypergraph model that jointly reasons over query text, evidence text, and video frames to detect misinformation in videos, preserving fi…
Clustering as Reasoning: A -Means Interpretation of Chain-of-Thought Graph Learning
Xuanting Xie, Zhaochen Guo, Bingheng Li +4
Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-…
Event-Aware Prompt Learning for Dynamic Graphs
Xingtong Yu, Ruijuan Liang, Renhe Jiang +4
Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural net…
PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
Yousef A. Radwan, Yao Li, Qing Qing +5
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of…