ISBN: 9783659941672 - enterprise risk prediction and interpretability research based on gnns (11 risultati)

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Condizione: New. In English.

Condizione: New.

Taschenbuch. Condizione: Neu. Enterprise Risk Prediction and Interpretability Research Based on GNNs | Huaming Du | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9783659941672 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. …

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PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 140 pp. Englisch.

Condizione: New. Print on Demand.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book integrates corporate financial risk research with graph neural network (GNN) technology to address the challenges of analyzing complex financial data and the interconnections between enterprises. It explores three key areas: 1. Dynamic Graph Representation: A framework for learning dynamic graph representations based on structural roles is proposed, capturing temporal evolution and global topological dependencies, marking the first use of recurrent learning in this context.2. Momentum Spillover Effects: A dual GNN algorithm is introduced to model the dynamic, complex inter-enterprise relationships and momentum spillover effects, offering a new approach to analyzing their impact on securities market volatility.3. Financial Risk Interpretability: To overcome the black-box nature of deep learning models, a heterogeneous GNN framework is developed to generate evidence subgraphs that reveal internal and external factors affecting enterprise financial risk, enhancing model transparency. Experimental results validate the proposed methods, showing improvements across multiple tasks, while also significantly enhancing model interpretability with faster inference times. …

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book integrates corporate financial risk research with graph neural network (GNN) technology to address the challenges of analyzing complex financial data and the interconnections between enterprises. It explores three key areas: 1. Dynamic Graph Representation: A framework for learning dynamic graph representations based on structural roles is proposed, capturing temporal evolution and global topological dependencies, marking the first use of recurrent learning in this context.2. Momentum Spillover Effects: A dual GNN algorithm is introduced to model the dynamic, complex inter-enterprise relationships and momentum spillover effects, offering a new approach to analyzing their impact on securities market volatility.3. Financial Risk Interpretability: To overcome the black-box nature of deep learning models, a heterogeneous GNN framework is developed to generate evidence subgraphs that reveal internal and external factors affecting enterprise financial risk, enhancing model transparency. Experimental results validate the proposed methods, showing improvements across multiple tasks, while also significantly enhancing model interpretability with faster inference times.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch. …