1 Introduction

As economic globalization and regional integration progress, the swift advancements in modern communication and high-speed transportation technologies have propelled cities and regions into a sophisticated stage of networked development. This evolution has fostered complex interconnections among urban areas (Batty, 2013; Beaverstock et al., 2000; Derudder & Neal, 2018). Increasingly, scholars from urban planning and geography fields are focusing on urban networks, gradually establishing this focus as a new paradigm for assessing regional development (Shan et al., 2023). Scholars have utilized a variety of relational data sources, such as infrastructure lines (Derudder & Witlox, 2008; Liu et al., 2016), socio-economic connections (Chen & Jiang, 2022; Taylor et al., 2002, 2014), and factor flows (Cao et al., 2022), to analyze urban networks at different scales. On this basis, they have explored the urban networks’ spatial pattern characteristics (De Goei et al., 2010), spatial-temporal evolution trends (Dai et al., 2023), growth dynamics mechanisms (Yao & Li, 2022), externalities, and spillover effects (Mao et al., 2023).

Global urban development faces a multitude of intricate and unprecedented challenges. Cities worldwide are grappling with risks associated with recurring extreme natural events, such as extreme temperatures, heavy rainfall, floods, and droughts, all of which significantly threaten urban safety (Koks et al., 2019). Simultaneously, the socioeconomic environment is undergoing a subtle yet profound transformation, driven by several factors like the reshoring of manufacturing industries to the United States, trade tensions between China and the United States, the extensive impacts of the COVID-19 pandemic, and the ongoing conflict between Russia and Ukraine (AlKhatib et al., 2020). In response to these dynamics, the focus of urban development is shifting strategically from a traditional emphasis on "growth and efficiency" to a prioritization of "safety and stability". This strategic realignment is mirrored in urban studies, where there is a growing focus on enhancing urban and regional resilience (Liu et al., 2022; Ribeiro & Gonçalves, 2019; Sharifi, 2019).

The term "resilience" derives from the Latin "resilio", meaning "to jump back" (Klein et al., 2003). Urban network resilience is a concept that has been developed by incorporating resilience into the study of urban networks (Champlin et al., 2023). This concept is utilized to examine the capacity of regional urban systems to withstand various disturbances, which has become a popular research topic in recent years (Li et al., 2021; Wei & Xiu, 2020; Zhao et al., 2020). Despite extensive research in this field, several limitations persist. Predominantly, most of the research consists of disparate case studies, lacking a cohesive synthesis. Additionally, the definition of "urban network resilience" varies significantly across different academic works, resulting in conceptual ambiguity. This study undertook a comprehensive literature review, categorizing the concept of urban network resilience identified in prior studies into two types. It further examined the research methodologies, structural measurement indicators, and optimization strategies employed in research of urban network resilience. Finally, the study concluded by delineating potential directions for future research on urban network resilience, aiming to guide further scholarly investigation in this field.

2 From urban network to urban network resilience

2.1 What is urban network?

In the context of economic globalization and regional integration, cities are increasingly engaging in various interactions. As a result, cities have become interconnected and have established diverse relationships with one another, giving rise to the emergence of urban networks spanning different spatial dimensions (Derudder and Neal, 2018; Pflieger and Rozenblat, 2010). Recently, scholarly attention in urban planning and geography has increasingly turned toward urban network research, heralding a new paradigm for assessing regional development (Shan et al., 2023). Scholars refer to spatial entities of different scales as nodes and the diverse interaction connections between these entities as edges to construct urban networks. This approach redefines regional research concerns into network-focused inquiries, enabling exploration through network analysis methodologies.

Given the complexities of urban networks, they can typically only be constructed from specific perspectives by utilizing one or several types of intercity interaction data (Hu and Lu, 2020). This leads to a diverse range of urban network configurations (Cao et al., 2022; Chen and Jiang, 2022; Derudder and Witlox, 2008), which can be broadly categorized into physical and organizational networks (Ma and Li, 2012; Malecki, 2002). Physical networks include various intercity infrastructure networks such as transportation, communication, energy networks, and ecological networks connecting different urban areas, all highly reliant on physical space. In contrast, organizational networks are intercity functional correlation networks based on the socio-economic connections among actors across cities and are less dependent on physical space, characterized by their flexibility and capacity for self-regulation (Mäkinen, 2001). These two types of urban networks significantly influence each other: the physical network underpins the organizational network, enhancing its efficacy, while the organizational network influences the development and evolution of the physical network.

2.2 Defining urban network resilience

Although urban network resilience has attract a wide range of attention, its definition is still unclear. Through a review of existing literature, we have identified two distinct concepts of urban network resilience. The conceptual nuances between these two types will be explored in the following section.

2.2.1 Resilience from urban network

Several studies have described urban network resilience as the ability to anticipate, withstand, react to, and adjust to the effects of various disturbances, as well as to recover or evolve by leveraging cooperative relationships with other cities in a specific area (Sheng et al., 2023; Wang & Ge, 2023; Wang et al., 2023). In this definition, resilience is viewed as an externality of urban network, which we refer to as resilience from urban network (RFUN). It is crucial to recognize that urban networks have a dual impact on how cities interact with disturbances (Foti et al., 2013) (see Fig. 1). Initially, studies primarily highlighted the positive externalities of urban networks, focusing on their role in enhancing the capacity of cities to deal with the disturbance. However, there has been increasing interest in the negative spillover effects of urban networks in recent years, emphasizing the necessity to better understand and mitigate the potential of urban networks to exacerbate the repercussions of disturbances (Kraemer et al., 2020; Von Braun, 2008).

Fig. 1
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Illustration of the concept of RFUN.

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Urban networks exhibit positive externalities when confronted with both short-term shocks and long-term growth challenges. In instances of abrupt upheavals like natural calamities or unforeseen incidents, urban networks enable cities to access resources such as personnel, supplies, information from neighboring cities. This support facilitates individuals, businesses, and governmental sectors in affected cities to improve their adaptive capabilities during the disruptive phase and expedite their recovery process thereafter (Champlin et al., 2023; Peng et al., 2017). For instance, the interconnected medical resources and workforce across different cities in China played a pivotal role in effectively containing the COVID-19 pandemic. Moreover, in response to persistent challenges like technological transformations and industrial renewal, the positive externalities of urban networks are evident in their capacity to facilitate the flow of knowledge, technology, and talents among cities. This exchange can continually enrich the city’s knowledge pool, thereby preventing technological lock-in and supporting sustainable development. For instance, several studies indicates that well-established intercity innovation networks play a crucial role in bolstering regional economic resilience (Boschma, 2015; Tóth et al., 2022).

The negative externalities of urban networks manifest in two ways. First, urban networks can serve as channels for the spread of harmful factors, such as pathogens, hazardous materials, and negative public sentiments, enabling their propagation across vast distances with pronounced impacts. For example, during the COVID-19 pandemic, urban agglomerations and megacities, characterized by significant population mobility, were especially vulnerable. The frequent movement of people between cities intensified the spread of the virus (Coven et al., 2023; Niu et al., 2021). Second, the development of urban networks increases the interdependence among cities, which heightens their susceptibility to cascading failures of cities (Balsa-Barreiro et al., 2020; Che et al., 2022). A case in point is the 2016 earthquake in Kumamoto, Japan, which disrupted local automobile manufacturing facilities. This event initiated a chain reaction throughout the supply chain, culminating in a substantial reduction in national automobile production levels (Klimova & Akimova, 2011).

2.2.2 Resilience of urban network

Some studies regarded urban network resilience as the ability of urban networks to withstand disturbances and maintain structural or functional integrity (Shan et al., 2023; van Ginkel et al., 2021; Wei & Pan, 2021). In this definition, resilience is identified as an intrinsic attribute of urban networks, referred to as resilience of urban network (ROUN). Like other networks, urban networks can be damaged by various disturbances, yet those that maintain their structural or functional integrity are deemed resilient. As mentioned above, urban networks can be categorized into two types: physical and organizational networks. The typical disturbance faced by these two types of urban networks may vary, leading to differences in how they respond and evolve in response to these disturbances (Ma & Li, 2012b). Therefore, we analyzed the resilience of each type of urban network separately (Fig. 2).

Fig. 2
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Illustration of the concept of ROUN.

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Physical networks, characterized by strong spatial dependence, are mainly influenced by disturbances that impact the physical environment, such as earthquakes, floods, armed conflicts, etc. (van Ginkel et al., 2022; Wei et al., 2022). Due to their physical nature, it is typically straightforward to notice and repair structural and functional issues in a physical network within a short period. Therefore, the resilience of physical networks can be understood from the perspective of engineering or ecological resilience as the ability to withstand physical damage and sustain a certain level of connectivity to ensure the continuous flow of various factors between cities (Wang et al., 2019).

Organizational networks can be influenced by disturbances in two ways. On the one hand, disturbances that impact the physical environment may pose indirect effects on organizational networks. For example, severe weather conditions such as heavy rainfall and flooding may interrupt intercity transportation lines, thereby hindering the movement of individuals and goods among cities (van Ginkel et al., 2022). On the other hand, disturbances such as economic downturns, regional industrial shifts, market player relocations, and epidemics can have the potential to destabilize intercity organizational networks without causing damage to the physical environment (Kim & Jun, 2022). Unlike physical networks, disturbances typically do not alter the configuration of organizational networks directly. Instead, disturbances impact organizational networks indirectly by changing the behaviors of various actors within the networks. The response to disturbances involves a time lag, as numerous actors adjust their behaviors, making the effects on the organizational network potentially delayed. Once established, these behavioral patterns are challenging to reverse, thus resulting permanent changes in organizational networks (Burger & Meijers, 2016). Therefore, the analytical framework of evolutionary resilience is better appropriate for analyzing the resilience of organizational networks. From this perspective, the resilience of organizational networks can be defined as the capacity to sustain current intercity collaborative relationships when faced with specific natural disturbances or shifts in the social and economic landscape, while also continuously improving network configuration to facilitate long-term regional advancement.

2.3 Relationship between RFUN and ROUN

Based on the analysis above, we found that the emergence of two different definitions of urban network resilience stems from varying understandings of urban networks (Fig. 3). Research adopting the RFUN perspective viewed urban networks as complementary and collaborative relationships among cities. It was clear that such studies deem urban networks as unaffected under disturbances and the main concern was the performance of intercity relationship for supporting affected cities to handle and adjust to various disturbances. Moreover, these studies enhanced our understanding of the factors influencing regional resilience from a relational perspective, contributing to the existing research that typically concentrates on attribution factors (Balland et al., 2015; Hu et al., 2021; Liu et al., 2021). The studies oriented around ROUN perceive urban networks as symbols of specific regions from a spatial structure perspective (Yang et al., 2011). In contrast to RFUN studies, ROUN studies focused on the susceptibility of urban network structure to disruption, aiming to maintain their structural or functional integrity. In addition, this viewpoint enabled the utilization of network analysis methods (including complex network analysis and social network analysis) to achieve a more detailed examination of regional resilience (Shan et al., 2023; Wang et al., 2024; Wang et al., 2019).

Fig. 3
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Illustration of the relationship of RFUN and ROUN.

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Furthermore, it is crucial to recognize that the two definitions of urban network resilience discussed above are interconnected. Typically, when cities encounter disturbances, both the individual cities and the urban network structure are affected simultaneously. Only urban networks that operate effectively under disturbances can demonstrate their externalities. Thus, ROUN serves as the basis for RFUN. On the other hand, RFUN reflects the effective cooperation mechanisms among cities to some extent, which can also be conducive to enhancing the ability of urban networks to deal with disturbances. In other words, RFUN can also impact ROUN.

3 Research progress in urban network resilience

After distinguishing the two concepts of urban network resilience, this study attemptted to provide a summary of previous studies in terms of research methodologies, characteristic indicators of resilience, and strategies for enhancing resilience (Fig. 4).

Fig. 4
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Key advancements in the study of urban network resilience.

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3.1 Methodology of urban network resilience research

RFUN and ROUN represent distinct concepts within the realm of urban network resilience, each with markedly different research methodologies. RFUN, essentially a type of urban network externality, primarily focuses on whether urban networks can provide sufficient support for cities within it to cope with various disturbances. In pursuit of this, several scholars utilized static structural indicators to describe network characteristics that might affect RFUN’s performance (Fang et al., 2023; Peng et al., 2018; Xu et al., 2022). Although these studies were inspiring, they primarily derived the relationships between various network structures and urban/regional resilience based theoretical analyses. To address those limitations, some scholars have applied a variety of econometric models to demonstrate and quantify the impact of urban networks on urban resilience (Balland et al., 2015; Kharrazi et al., 2017). For instance, Balland et al. executed a thorough analysis of how the flexibility of innovation collaboration networks influences the resilience of innovation outcomes across 366 metropolitan areas in the United States (Balland et al., 2015). Kharrzi et al. explored the interplay between the structural configuration of international trade networks and their resilience during the global financial crisis (Kharrazi et al., 2017).

On the other hand, ROUN is essentially the intrinsic structural resilience of urban networks. Research on ROUN primarily investigated whether urban networks can maintain their structural and functional integrity when faced with disturbance. Such studies mainly focused on accessing the robustness of physical networks against short-term shocks and the transformability of organizational networks to long-term changes. Although static structure measures were also utilized, the primary method for analyzing ROUN were dynamic analysis approaches. By examining how the key structural and functional characteristics of urban networks change under simulated or actual disturbance conditions, the ROUN was measured in a dynamic way. Due to challenges in obtaining real-time monitoring data for actual disturbances, simulated scenarios were more frequently used in existing studies. In terms of scenario simulation, scenarios involving random failures and deliberate attacks are particularly prevalent (Burleson-Lesser et al., 2020). While nodes and edges are randomly removed to simulate random failures triggered by disturbances like natural disasters or public health emergencies, edges were sequentially removed to simulate deliberate attacks such as armed conflicts or technological embargoes (Albert et al., 2000; Bombelli et al., 2020)s. Moreover, several studies identified pivotal and susceptible nodes or links by examining the impacts of specific node or link failures within urban networks (Karakoc et al., 2023; Peng et al., 2019).

3.2 Static structural indicators of urban networks resilience

Both RFUN and ROUN studies used static structural indicators to measure urban network resilience. However, as previous sections have already demonstrated the distinctions between the two definitions of urban network resilience, it is crucial to determine how the commonly used indicators represent urban network resilience and whether the resilience measured is ROUN or RFUN.

RFUN, a type of urban network externality, can be measured at both the individual city level and the overall network level (Fang et al., 2023; Peng et al., 2018; Xu et al., 2022). For individual cities, urban networks serve as conduits to access external resources. The degree of a city’s integration into urban networks can directly influence the support it receives from other cities and is often measured by indicators such as degree, closeness, and betweenness centrality. Cities with higher centrality are generally more interconnected with other cities, allowing for more efficient access to external resources. This high centrality helps mitigate the effects of disruptions, minimize losses, and accelerate recovery (Che et al., 2022; Karakoc et al., 2023). Therefore, those nodes are regarded as resilient in terms of RFUN. For the entire region, urban networks play a crucial role in optimizing the distribution of regional resources, thereby bolstering the region’s ability to manage disturbances effectively. Global structure indicators that signify the level of hierarchy (degree distribution coefficient) and assortativity (degree correlation coefficient) are most utilized to measure RFUN at the overall network level. Most scholars believe that urban networks characterized by core-peripheral and disassortative patterns, which have high degree distribution and negative degree correlation, are considered resilient from the RFUN perspective (Crespo et al., 2014; Fang et al., 2023; Shi et al., 2022). These network patterns facilitate the efficient distribution of elements and enhance exchanges between core and peripheral areas, enabling quick responses to sudden disruptions in certain cities and preventing long-term technological lock-in issues. Indicators signifying the agglomeration level of the entire urban network are also utilized in RFUN studies. While some scholars argue that high agglomeration fosters a trust-based environment beneficial for RFUN, others caution that excessive agglomeration may lead to regional technological lock-in risk, which is harmful to RFUN.

ROUN, the structural resilience of urban networks, encompasses both robustness against shocks and transformability for long-term development. The robustness of urban networks is often accessed by the level of structural redundancy and modularity. Key indicators for assessing network redundancy include network density, average number of independent paths, and standard network structure entropy (Hou & Sun, 2022; Zhang & Huang, 2022), while the modularity coefficient is often utilized to characterize the network’s modularity level (Zhang et al., 2021). Furthermore, some researchers have applied hierarchy and transmission indicators, commonly employed to characterize RFUN, to describe ROUN. However, the explanations for why urban networks with high hierarchy and transmission levels are considered resilient from the ROUN perspective vary. For example, it has been highlighted that due to the prominence of the key components, networks with pronounced hierarchical structures are resilient to random failures but vulnerable to deliberate attacks (Wei & Pan, 2021). Additionally, urban networks with high transmission levels are deemed resilient due to fewer intermediate nodes between each node pair, reducing the potential for link interruptions (Guo et al., 2022). Despite these insights, the long-term transformability of urban networks has garnered less attention. Only a few scholars have attempted to analyze the transformability of the urban network, using the indicators of structural equilibrium and reciprocity to characterize urban network resilience. It is argued that urban networks with higher balanced and reciprocal structures are more favorable for the long-term development of synergistic or complementary urban networks(Li & Zhen, 2023; Zhang & Huang, 2022).

3.3 Strategies for improving urban network resilience

The ultimate objective of urban network resilience research is to optimize the layout of urban networks to support safe regional development. Within the realm of RFUN research, the key aim is to enhance urban networks’ positive impact on facilitating regional cooperation in handling disruptions, while minimizing their negative effects on the spread of disturbances and chain reactions. Strategies proposed to achieve this goal often emphasize enhancing connectivity and efficiency within urban networks, as well as establishing smoother connections between central and peripheral nodes (Peng et al., 2018; Ye & Qian, 2021). In the current era of economic globalization and regional integration, collaboration among cities is not only advantageous but also an irreversible trend in urban development. Therefore, a cautious approach is necessary to mitigate potential negative consequences associated with urban networks to ensure the safe development of interconnected urban areas. In response to the impact of the COVID-19 pandemic, numerous studies have explored the relationship between intercity human mobility and virus spread (Boyce & Katz, 2021; Coven et al., 2023; H. Zhang et al., 2023), suggesting that developing polycentric urban networks and establishing robust surveillance systems for monitoring intercity resource exchanges could effectively counter the negative impacts of urban networks (Jia et al., 2020; Niu et al., 2021).

From the perspective of ROUN, the primary objective of urban network optimization is to bolster the refinement of the network structure, ensuring its ability to withstand shocks while continuously transforming to meet the long-term development requirements of cities within them. Previous research predominantly concentrates on strategies to improve the robustness of urban networks, with scholars commonly asserting that the redundancy of nodes and connections plays a pivotal role in enhancing urban networks’ resilience to disruptions (Xu et al., 2021). Additionally, it is suggested that establishing a polycentric and flat spatial configuration can effectively mitigate disturbances that may disrupt the spatial organization pattern (Wang et al., 2024). Furthermore, it is also of great significance to safeguard the key nodes and edges within the network, maintaining their secure connections with other cities in the region to reduce the likelihood of urban nodes becoming isolated from the broader network (Peng et al., 2019; Yang et al., 2022).

4 Future research agenda of urban network resilience

4.1 Toward in-depth resilience connotation of various urban networks

By integrating studies on urban networks and resilience, research on urban network resilience can shed light on traditional urban network studies that overlook potential disturbances, as well as on traditional urban and regional studies that solely focus on attributive factors. Currently, research on urban network resilience is still in its early stages, with prevalent confusion due to varied definitions of the concept. We identified two distinct types of urban network resilience concepts that have emerged in prior research: RFUN and ROUN. From the RFUN perspective, urban networks are viewed as pathways for affected cities to receive support from others. These researches emphasize minimizing losses for both the impacted cities and the entire region by fostering collaborative intercity relationships. The ROUN perspective focuses on the structural resilience of urban networks when faced with disturbances, focusing on their capacity to absorb and recover from disturbances.

Further investigation is required to explore how urban networks respond to various real disturbances, thereby deepening our comprehension of urban network resilience (Meerow et al., 2016). A key question in this field is to determine the primary focus of resilience and the role urban networks play in specific disturbance scenarios. By elucidating the concept theoretically, the integration of resilience into urban network research can be further advanced. Furthermore, recognizing the unique characteristics of different urban networks is vital in such research. Especially for organizational networks, it is essential to thoroughly evaluate the data types employed in constructing the network and the nature of depicted intercity connections. Conducting resilience analyses of urban networks based on these considerations is crucial to ensure that the research outcomes offer valuable insights for relevant policy decision-making.

4.2 Optimize research methodology for urban network resilience

Although there are already a considerable number of studies exploring urban network resilience (Bombelli et al., 2020; Crespo et al., 2014; Karakoc et al., 2023), the research methodologies in this field remain underdeveloped. Enhancements are necessary in the construction of urban networks, the selection of characteristic indicators, and the design of measurement methods.

Firstly, it is advisable to incorporate more details when constructing urban networks. A prevalent limitation in existing studies is the reliance on a simplistic binary network model that overlooks the varying strengths of connections within the networks (Wang et al., 2023). In reality, urban networks are typically heterogeneous and asymmetric, meaning that interactions between cities often exhibit diverse and unequal intensities (Kharrazi et al., 2017). Therefore, future research should integrate the specific characteristics of nodes and edges and adopt a weighted network model in urban network resilience research. This methodological approach is expected to increase the depth of analysis, leading to more valuable and insightful findings for enhancing urban network resilience (Wei & Xiu, 2020).

Secondly, future research should validate the effectiveness of diverse network structure indicators for representing urban network resilience, both theoretically and empirically. Currently, numerous researchers employ similar indicators to describe structure characteristics related to urban network resilience, frequently overlooking the subtle differences across various urban networks. Moreover, given the inherent disparities among types of urban networks, employing a uniform set of indicators is not only inappropriate but can also be problematic. Therefore, it is imperative to strengthen empirical studies on the relationship between urban network structure indicators and resilience, and thus provide more tailored and rational indicator sets for the measurement of urban network resilience.

Thirdly, existing research has primarily focused on evaluating the robustness of urban networks. However, resilience encompasses a broader range of characteristics beyond the robustness. Future studies should enhance measurement techniques to capture the full spectrum of urban network resilience. For physical urban networks, it is crucial to assess their ability to maintain and restore functionality by considering the intercity flows they facilitate (Goldbeck et al., 2019). For organizational urban networks, adopting an analytical framework of evolutionary resilience can help analyze how different disturbances impact the formation, maintenance, and expansion of intercity connections over time (Marull et al., 2015). Moreover, the prevalent dynamic simulation approach utilized in prior research is adapted from studies on infrastructure network resilience assessment, demonstrating applicability to regional physical networks. While this method has been employed in evaluating the resilience of organizational networks, there is a requirement for additional investigation into the optimal design of disturbance scenarios and the interpretation of measurement results.

4.3 Conduct resilience research on interdependent urban networks

Currently, studies on urban network resilience tend to concentrate on a single type or scale of urban network. However, various urban networks are interconnected and interdependent (Kenett et al., 2014; Y. Li and Shi, 2015). For instance, the intercity infrastructure network serves as a foundation for the establishment and evolution of various intercity organizational relationships. The organizational network can also be influenced by the continuous evolution of the physical network. Additionally, there are intricate interactions among urban networks of different scales (Pflieger & Rozenblat, 2010). It is commonly recognized that in the era of globalization, the expansion and evolution of local or regional urban networks are greatly influenced by urban networks at national or global scales (Li and Phelps, 2018). Therefore, it is plausible to anticipate that under disturbance scenarios, risk factors will disseminate across various types and scales of urban networks, potentially leading to devastating cascading failures (Goldbeck et al., 2019; Havlin and Kenett, 2015). Consequently, focusing solely on a single type or scale in resilience research may introduce biases, potentially impeding a comprehensive analysis of the factors influencing urban network resilience and even leading to misinterpretations.

To achieve a more holistic understanding, future research should not only explore the resilience connotation, characteristics, and optimization strategies of specific types of urban networks but also consider their interdependence and coordination. This requires a detailed theoretical examination of the correlations between different types and scales of urban networks. Concurrently, there is a need to refine the methodologies of network construction and resilience measurement for multi-type and multi-scale complex urban networks, laying the groundwork for empirical studies (Caschili et al., 2015). Such studies are beneficial for thoroughly understanding how disturbances affect regional urban systems and can provide a scientific reference for policy decision-making in urban planning, emergency management, and other related fields.

5 Conclusion

Urban network resilience has attracted considerable interest from researchers across various disciplines. The purpose of this research was to clarify the definition of urban network resilience, summarize the research methodologies, structural measurement indicators, and optimization strategies employed in existing studies, and thus identify potential research gaps for future studies.

Through a comprehensive literature review, this paper categorized the definition of urban network resilience in relevant studies into two types: RFUN and ROUN. Urban networks, viewed as conduits through which cities garner support from others, position RFUN as a type of urban network externality related to resilience. Studies have characterized RFUN using static structural indicators based on theoretical analysis, assessed the impact of urban networks on urban or regional resilience using econometric methods, and recommended strategies to harness intercity collaborative relationships to minimize losses. On the other hand, scholars advocating the ROUN perspective interpret urban networks as symbols of urban systems from a spatial structure standpoint, considering ROUN to be an intrinsic structural characteristic of urban networks. Relevant studies have analyzed the structural resilience of various urban network types using both static indicators and dynamic simulation methods, subsequently proposing strategies to assist urban networks in maintaining their structural and functional integrity.

We recommended that future research deepen the interpretation of urban network resilience by examining the role of urban networks with distinct characteristics in real-world disturbance scenarios. Adopting a weighted network model could enable a more detailed integration of the varied strengths of intercity connections. Additionally, it is suggested to develope and utilizemore precise and appropriate indicator sets tailored to the specific resilience of specific urban networks, and create rational evaluation techniques to capture the entire spectrum of urban networks. Given the increasing interdependencies among various types and scales of urban networks, it is essential to study the resilience of multi-type and multi-scale complex urban networks to provide more practical insights for policy decision-making.