Introduction 
Contemporary cities are more interconnected than ever with complex networks of multidimensional economic, social, and technological relationships. In this view, a city’s status is not static; it arises from a continual coevolutionary process that spans multiple network layers and intra-urban connection models. These multidimensional processes have transformed urban systems into dynamic and complex systems. They continually rearrange urban structures, creating new patterns of focus and defocus, as well as role formation, across cities. From a network perspective, two key concepts — centrality and network power — define an urban position. Centrality refers to a node’s position based on the number, intensity, and location of its links, offering a picture of connectivity and the relative position of cities. Power refers to a city’s ability to guide and monitor processes. It is horizontal and distributed, and each city holds a share according to its location. By classifying urban network centers by power and centrality, one can distinguish fully connected cities that lack strategic influence from low-centrality cities that hold a controlling or provincial role. The historical imprint of links, institutional constraints, and size-based and network-based contexts heavily influences these dynamic urban networks. The current research analyzes and determines the modern dynamics of Iran’s urban network in relation to the interactional approach of air flow from 1976 to 2016. It relies on centrality indices and recursive power at the small level, examines the overall network structure at the large level through free and small-world patterns, and uses the ERGM model. The contribution lies in the simultaneous analysis of long-term processes and the effects of institutional and contextual dependencies. The primary question aims to identify the central and powerful centers in Iran’s current urban network and to present the spatial patterns that shape these developments by linking them to the network’s internal, external, and historical influences.
Materials and Methods
This research uses a three-level design: (1) identifying hubs with standard scales of centrality and power; (2) representing structural transformation of the network with complex network indicators; and (3) calculating the intensity and direction of demographic, institutional, and contextual effects. The methodology is descriptive–analytical. Statistical data on domestic flights represent the air flow network. The analysis maintains validity by focusing on stable processes and interpreting results within a real-data framework. Two indices, recursive centrality (RC) and recursive power (RP), also measure the functional position of cities. Cities are classified into four functional groups using a four-section approach: control hubs, connecting centers, gateway cities, and peripheral cities. Gephi software supports the analysis of network structure by identifying features consistent with small-world and scale-free network models. The Exponential Random Graph Model (ERGM) explains the formation of network links. This approach detects hidden patterns in the creation or absence of connections by considering both internal structural factors (density and focused connections) and external conditions (population and the institutional status of cities), as well as historical dependencies. MPNet software estimates the model parameters. This framework offers a precise and multilayered examination of the role of cities within the urban network.
Findings
The findings indicate a shift in the network’s focal arrangement from a strictly Tehran-based core–periphery hierarchy to a mono-core pattern in power and a relatively polycentric pattern in centrality. Tehran remains a stable national mono-core, while Mashhad, Shiraz, Isfahan, Kish, and Ahvaz have advanced to full-connection hubs without a corresponding increase in influence or control power. At the same time, improvements across many peripheral cities have reinforced the Tehran-based hub-and-spoke configuration, which constrains balance in urban role formation and yields characteristics of a scale-free and small-world network at the level of the broader air-flow system. ERGM results show that Iran’s domestic flight network over this period reflects functional forces, such as the tendency to cluster around traffic hubs, as well as institutional and political factors, including the location of the capital and provincial centres, and historical dependence on past routes; each plays a consistent role in forming flight links. The population also serves as a key driver of route attraction and is showing growing importance. Overall, network development arises from the concurrent interaction of structural, institutional, contextual, and demographic forces.
Conclusion
This research uses a network-based framework to address theoretical shifts and practical needs in studies of inter-urban relations, clarifying the distinction between connectivity and control power across cities. Analysis of city locations and the overall structure of the domestic flight network provides multi-level views of spatial dynamics and rearrangements in the Iranian urban system for air flows. The role-and-location analysis reveals a clear pattern in the dynamics of the main hubs of the national air network. Over the last five decades, network centrality has moved away from an absolute focus and now distributes across many hubs. However, this shift has not produced control power. The persistence of mono-core power alongside the relative polycentricity of air flows is not an incompatibility but a direct outcome of two discernible processes that reproduce centrality and power in the network. A transition away from a focused structure in Iran’s air traffic network necessitates a reassessment of spatial policy and enhancements in the functions of secondary and intermediate hubs. Achieving a more balanced and durable network requires stronger horizontal links among regional clusters, reduced dependence on the central hub, and expansion of gateway cities with a control role. This transition requires an intervention that considers three key factors simultaneously: institutional attributes (such as provincial and political centrality), demographic patterns, travel demand, and the network’s historical inertia. By accounting for these contextual and persistent variables, planners can allocate flight capacity more intelligently, diversify routes, and strengthen links with few alternatives. To this end, institutional planning should be combined with a data-driven incentive mechanism, within a framework consistent with the network’s historical context. Such a process can connect decentralization to a real redistribution of power and improve the effectiveness and spatial stability of Iran’s urban system.