---
title: Enhancing Graph Network Models
url: https://www.ml-quant.com/papers/ssrn/4973874/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4973874
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4973874
featured: 2024-10-03
citations: unknown
topic: ML & AI Methods
---


# Enhancing Graph Network Models

The study investigates the use of metaheuristics such as Simulated Annealing, Tabu Search, and Variable Neighborhood Search to improve the efficiency of graph network models like Graph Neural Networks and Graph Convolutional Networks.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4973874
- Identifier: SSRN 4973874
- Released: 2024-09-12
- First featured: Quant Letter No. 68 (2024-10-03): https://www.ml-quant.com/issues/2024-10-03/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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