RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs
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Abstract (arXiv)
We propose RicciFlowRec, a geometric recommendation framework that performs root cause attribution via Ricci curvature and flow on dynamic financial graphs. By modelling evolving interactions among stocks, macroeconomic indicators, and news, we quantify local stress using discrete Ricci curvature and trace shock propagation via Ricci flow. Curvature gradients reveal causal substructures, informing a structural risk-aware ranking function. Preliminary results on S&P~500 data with FinBERT-based sentiment show improved robustness and interpretability under synthetic perturbations. This ongoing work supports curvature-based attribution and early-stage risk-aware ranking, with plans for portfolio optimization and return forecasting. To our knowledge, RicciFlowRec is the first recommender to apply geometric flow-based reasoning in financial decision support.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 12 Aug 2025.
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12 of 23 references.
References · 12
- 2017Graph Attention Networkscited by 8,923
- 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
- 2017Causal Embeddings for Recommendationcited by 79
- 2019Community Detection on Networks with Ricci Flowcited by 62
- 2015Ricci Curvature of the Internet Topologycited by 22
- 2024Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphscited by 6
- 2025Causal Spherical Hypergraph Networks for Modelling Social Uncertaintycited by 5
- 2025Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learningcited by 4
- 2024Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Samplingcited by 3
- 2025ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendationscited by 3
- 2019Stress Testing Network Reconstruction via Graphical Causal Modelcited by 1
- 2022Deep Causal Reasoning for Recommendationscited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.