FinSCRA: An LLM-Powered Multi-Chain Reasoning Framework for Interpretable Node Classification on Text-Attributed Graphs

FinSCRA: An LLM-Powered Multi-Chain Reasoning Framework for Interpretable Node Classification on Text-Attributed Graphs | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 February 2026 V1 Latest version Share on FinSCRA: An LLM-Powered Multi-Chain Reasoning Framework for Interpretable Node Classification on Text-Attributed Graphs Authors : Pengfei Pan , Lizi Chen , Qi He 0009-0000-6258-5137 [email protected] , Han Wang , Keyu Yuan , and Wenchao Zhang Authors Info & Affiliations https://doi.org/10.22541/au.177204304.42015977/v1 133 views 54 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Text-attributed graph node classification is still a challenge since it needs to reason about the topology structure simultaneously with the free-text semantics. Although graph neural network can perform well on structural propagation,they tend to be blind for the details in the text associated with nodes. On the other hand, LLMs have excellent NLU skills and are weak on structured,multi-hop reasoning over network agents.To address the above gap, in this work we propose FinSCRA, a novel LLM-powered multi-chain reasoning framework to inject domain-aware reasoning capability into a financial LLM with parameters efficient fine-tuning. Specifically, our framework designs a hierarchy of structured reasoning chains (singlehint,parallel, cascaded, and hybrid methods to extract and fuse the semantic signals like sentiment, correlation, and risk signals in the nodes' text.A fusion layer based on fuzzy logic fuses the results of different reasoning lines for better robustness and explainability.While FinSCRA is generic and can be applied to other types of text-attributed graphs, here we assess its performance on credit risk analysis in supply chain networks,on the task of entity relation extraction, in which entities are related through their financial relation and described with rich text reports; we show experimentally on realworld datasets that our model FinSCRA greatly outperforms graphbased as well as LLM-based baselines,as an accurate and explainable technique to perform node classification over complex networked systems.We release our code and models for further research on LLM-grap. Supplementary Material File (finscra an llm-powered multi-chain reasoning framework for interpretable node classification on text-attributed graphs.pdf) Download 1.39 MB Information & Authors Information Version history V1 Version 1 25 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords credit risk explainable ai finllm supply chain risk Authors Affiliations Pengfei Pan View all articles by this author Lizi Chen View all articles by this author Qi He 0009-0000-6258-5137 [email protected] View all articles by this author Han Wang View all articles by this author Keyu Yuan View all articles by this author Wenchao Zhang View all articles by this author Metrics & Citations Metrics Article Usage 133 views 54 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Pengfei Pan, Lizi Chen, Qi He, et al. FinSCRA: An LLM-Powered Multi-Chain Reasoning Framework for Interpretable Node Classification on Text-Attributed Graphs. Authorea . 25 February 2026. DOI: https://doi.org/10.22541/au.177204304.42015977/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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