Optimizing Network Topology Visualization: A Synergistic Framework for Global Convergence and Visual Expression Enhancement

In the era of big data, network topology visualization technology is crucial for the analysis of social networks, communication networks and other fields. Aiming at the problem that traditional force-directed algorithms are not efficient enough in complex networks and easy to be trapped in local optimums, this paper proposes an improvement method that integrates dynamic node importance modeling and multi-objective collaborative optimization. The edge-crossing penalty function is designed by geometric distance inverse weighting to significantly reduce the visual chaos in dense regions. the combination of dynamic centrality quantization model and adaptive force field mechanism realizes the visual reinforcement of key nodes and the enhancement of community structure cohesion. Experimental results demonstrate the method's enhanced convergence efficiency and superior global optimization capabilities in complex network environments. The proposed technique significantly improves topological clarity and layout stability while offering an effective visualization framework for comprehensive network analysis.