Abstract: Objectives: The International Conflict Network (ICN) reflects geopolitical conflicts manifested through news interactions among countries/regions, while the International Aviation Network (IAN) mirrors aviation relationships between countries/regions. We aim to reveal the temporal and spatial evolution of ICN and IAN, and further analyze the correlation between the two networks. Methods: We utilized data from the Global Database of Event Language and Tone (GDELT) and the Official Aviation Guide (OAG) of 2022, then extracted time-series data on news conflicts and air traffic in this year. As the conflict between Russia and Ukraine is a typical international emergency in 2022, which has a far-reaching impact on the international relation pattern evolution, we constructed the ICNs and IANs respectively based on the different stages of the development of bilateral relations between the subject of the event, so as to more accurately reveal the dynamic characteristics and association between the two networks in the specific historical context. Specifically, the geometrically mapping inspired multivariate changepoint detection algorithm is used to define the various stages of the development of the special international event. On this basis, we employed heatmap technology to analyze the spatiotemporal evolution of network hotspots. From both global and local perspectives, we utilized correlation analysis methods including Jaccard similarity and correlation coefficients, as well as causal analysis methods including Convergent Cross Mapping (CCM) and Extended Convergent Cross Mapping (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 anchored in Europe, North America, and the United Arab Emirates. (2) The evolving trends of local subnetworks centered on the special international event entities effectively reflect changes induced by the conflict: the two subnetworks demonstrate contrasting evolutionary patterns. The ICN subnetwork progressively expands, with its node count (peak value) growing to 204.3% and edge count (peak value) to 273.8% of pre-conflict levels, while the IAN subnetwork gradually contracts, decreasing to 82.6% of original nodes and 61.4% of edges. (3) Compared with traditional correlation-based network analysis methods, the causal inference framework substantially deepens time-series analysis of complex networks: conventional correlation metrics struggle to capture the evolution characteristics of ICN and IAN 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: taking Russia as an example, the causal effect of its international conflict intensity on international aviation intensity is 0.3, while the reverse causal effect is 0.7, revealing the limited impact of geopolitical conflict information on Russia's international aviation industry and the strong influence of Russia's aviation crisis on its international public image. Notably, CCM requires no lag-time specification, whereas ECCM better detects fixed-lag causal relationships. Conclusions: We integrates complex network with nonlinear time series analysis, establishing a "dynamic coupled network-association analysis" framework to reveal 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.