Objectives The international conflict network (ICN) reflects geopolitical conflicts manifested through news interactions among countries or regions. While the international aviation network (IAN) mirrors aviation relationships between countries or regions. We aim to reveal the spatial-temporal evolution of ICN and IAN, and further analyze the correlation between the two networks.

Methods First, we utilize data from the global database of event language and tone and the official aviation guide of 2022. Then, we extract time-series data on news conflicts and air traffic in this year. The conflict between Russia and Ukraine in 2022 is a typical international emergency, with far-reaching impacts on the evolution of international relations. We construct the ICN and IAN respectively based on the different phases of the development of this event, so as to reveal the dynamic characteristics and association between the two networks. Specifically, the geometrically mapping inspired multivariate changepoint detection algorithm is used to define the various phases of the development of the special international event. On this basis, we employ heatmap technology to analyze the spatiotemporal evolution of network hotspots. From both global and local perspectives, we utilize correlation analysis methods including Jaccard similarity and correlation coefficients, as well as causal analysis methods including convergent cross mapping (CCM) and extended CCM (ECCM), to reveal the association between the two networks.

Results (1) The response patterns of the two networks to the special international event exhibit significant heterogeneity. The ICN is relatively sensitive to the special international event, with its hotspots gradually concentrating in Russia, Ukraine, Palestine and Israel. The IAN demonstrates topological resilience, with its hotspots consistently anchoring in Europe, North America, and the United Arab Emirates. (2) The evolving trends of local subnets centered on the special international event entities effectively reflect changes induced by the conflict. The two subnets demonstrate contrasting evolutionary patterns. The ICN subnet progressively expands, with its peak node count growing to 204.35% and peak edge count to 273.77% of pre-conflict levels. While the IAN subnet gradually contracts, decreasing to 82.67% of original nodes and 61.40% of edges. (3) Compared to traditional correlation-based network analysis methods, the causal inference framework enhances time-series analysis of complex networks.Traditional correlation metrics struggle to capture the evolution characteristics of the ICN and IAN, which are driven by nonlinear dynamic mechanisms. In contrast, the empirical dynamic modeling-based CCM and ECCM causal analysis frameworks, requiring no model equations, prove more suitable for complex network association analysis. (4) Causal analysis reveals asymmetric bidirectional causal relationships between key nodes. For example, the causal effect of Russia's international conflict intensity on international aviation intensity is 0.3, while the reverse causal effect is 0.7. This suggests that geopolitical conflict information has a limited impact on Russia's international aviation industry, but Russia's aviation crisis strongly influences its international public image. Notably, CCM requires no lag-time specification, while ECCM is better suited for detecting fixed-lag causal relationships.

Conclusions We integrate complex network analysis with nonlinear time-series analysis to establish a "dynamic coupled network-association analysis" framework to reveal the global system evolution under geopolitical shocks. The revealed ICN-IAN evolutionary patterns under the special international event hold significant decision-making value for international conflict early warning and resilience governance of aviation networks.