Analysis and Risk Warning of Financial Markets Based on Communication Network Optimization Algorithms

Abstract

This paper uses communication network optimization algorithms to study financial market evolution and systemic risk warning. The Dijkstra algorithm optimizes the financial market network. Data processing like noise reduction improves risk analysis accuracy. The research focuses on systemic financial risks, covering their definition, measurement, warning mechanisms, and complex network model applications. A complex financial network framework is built. Methods such as optimal threshold determination and community detection are used to analyze market network characteristics in pre - pandemic, pandemic, and post - pandemic stages. The minimum spanning tree retains key risk paths, with optimal thresholds of 0.55, 0.62, and 0.67 for each stage. Community detection by the Fast - Newman algorithm shows pandemic impacts on financial market industry interconnections. Findings show pre - pandemic, the market was well - connected and stable. During the pandemic, connections weakened and risk isolation rose. Post - pandemic, the market recovered but not fully. Network topology indicators serve as risk warning metrics, with a 25% fluctuation threshold for early warnings. This research offers new ways to understand market vulnerabilities, optimize risk management, and forecast market trends during crises. The use of optimization algorithms and self - healing mechanisms in risk warning has great theoretical and practical value.