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Recently, the serviceability of the transportation infrastructure in urban areas has become crucial. Any impact of the hazardous conditions on the urban road network causes significant disruption to the functioning of the urban region, making the city’s resilience a point of concern. Thereby, the purpose of the study is to examine the city’s recovery capacity to absorb the impacts of adverse events like urban floods.

This study examines the road network resilience for an urban flood event for zones proposed by the Municipal Corporation to develop multiple central business districts. This study proposes a novel approach to measure the resilience of road networks in an urban region under floods caused due to heavy rainfall. A novel Road Network Resilience Index (RNRI) based on the serviceability of the road network during floods is proposed, estimated using Analytic Hierarchy Process - Multiple Criteria Evaluation (AHP-MCE) approaches by using the change in street centrality, impervious area and road network density. This study examines and analyses the resilience of road networks in two conditions: flood and nonflood conditions. Resilience was estimated for both the conditions at the city level and the decentralized zone level.

Based on RNRI values, this study identifies zones having a lower or higher resilience index. The central, southern and eastern zones have lower road network resilience and western and northern zones have high road network resilience.

The proposed methodology can be used to increase road network resilience within the city under flood conditions.

The previous literature on road network resilience concentrates on the physical properties of roads after flood events. This study demonstrates the use of nonstructural measures to improve the resilience of the road network by innovatively using the AHP-MCE approach and street centrality to measure the resilience of the road network.

Shortest distance query between a pair of nodes in a graph is a classical problem with a wide variety of applications. Exact methods for this problem are infeasible for large-scale graphs such as social networks with hundreds of millions of users and links due to their high complexity of time and space. The purpose of this paper is to propose a novel landmark selection strategy which can estimate the shortest distances in large-scale graphs and clarify the efficiency and accuracy of the proposed strategy in comparison with currently used strategies.

Different from existing strategies, the landmark selection problem is regarded as a binary combinational optimization problem consisting of two optimization objectives and one constraint. Further, the original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without any additional constraints and the equivalence of solutions is proved. Finally the solution of the optimization problem is performed with a modified multi-objective particle swarm optimization (MOPSO) integrating the mutation operator and crossover operator of genetic algorithm.

Four real networks of large scale are used as data sets to carry out the experiments and the experiment results show that the proposed strategy improves both of the accuracy and time efficiency to perform shortest distance estimation in large scale graph compared to other currently used strategies.

This paper proposes a novel landmark selection strategy which regards the landmark selection problem as a binary combinational optimization problem. The original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without constraints and the equivalence of these two optimization problems is proved. This novel strategy also utilizes a modified MOPSO integrating the mutation operator and crossover operator of genetic algorithm.

The most sustainable forms of urban mobility are walking and cycling. These modes of transportation are the most environmental friendly, the most economically viable and the most socially inclusive and engaging modes of urban transportation. To measure and compare the effectiveness of alternative pedestrianization or cycling infrastructure plans, the authors need to measure the potential flows of pedestrians and cyclists. The paper aims to discuss this issue.

The authors have developed a computational methodology to predict walking and cycling flows and local centrality of streets, given a road centerline network and occupancy or population density data attributed to building plots.

The authors show the functionality of this model in a hypothetical grid network and a simulated setting in a real town. In addition, the authors show how this model can be validated using crowd-sensed data on human mobility trails. This methodology can be used in assessing sustainable urban mobility plans.

The main contribution of this paper is the generalization and adaptation of two network centrality models and a trip-distribution model for studying walking and cycling mobility.

While previous literature has emphasized the causal relationship from liquidity to capital, the impact of interbank network characteristics on this relationship remains unclear. By applying the interbank network simulation, this paper aims to examine whether the causal relationship between capital and liquidity is influenced by bank positions in the interbank network.

Using the sample of 506 commercial banks established in 28 European countries from 2001 to 2013, the author adopts the generalized method of moments simultaneous equations approach to investigate whether interbank network characteristics influence the causal relationship between bank capital and liquidity.

Drawing on a sample of commercial banks from 28 European countries, this study suggests that the interconnectedness of banks within interbank loan and deposit networks shapes their decisions to establish higher or lower regulatory capital ratios in the face of increased illiquidity. These findings support the implementation of minimum liquidity ratios alongside capital ratios, as advocated by the Basel Committee on Banking Regulation and Supervision. In addition, the paper underscores the importance of regulatory authorities considering the network characteristics of banks in their oversight and decision-making processes.

This paper makes a valuable contribution to the current body of research by examining the influence of interbank network characteristics on the relationship between a bank’s capital and liquidity. The findings provide insights that add to the ongoing discourse on regulatory frameworks and emphasize the necessity of customized approaches that consider the varied interbank network positions of banks.

The purpose of this paper is to investigate the impact of large‐scale collaboration network structure on firm innovation capability.

The authors utilize negative binomial regression models to explore the relationships between network structure and patent productivity.

Results demonstrate that firms embedded in alliance networks with higher reach will have greater innovative output, but fail to support the higher clustering enhances innovative productivity significantly, which is different from recent concerns regarding positive effects of higher clustering on patent output. The authors also find that the effect of alliance network as a conduit of knowledge transfer decays over time. Furthermore, both clustering and reach have mutually positive effects on the firm innovation with increases in the other.

Network structure attribute should be considered adequately when firms develop alliance activities and when relevant government departments make industrial policies.

This paper aims to fill the gap in the literature by providing the first systematic research of the impact of large‐scale collaboration network structure on firm innovation in China, and exploring the implications for firms to develop alliance activities and for relevant government departments to make industrial policies. This paper adds new evidence to the topic that alliance network is an important mechanism of knowledge spillover in China.

Networks linking tourist attractions or organizations are a major focus of tourism research. Despite extensive research on tourism networks, academic research on the spatial structure and formation of wine tourism networks is limited. This study aims to investigate the spatial structure and factors influencing the development of a network among Ningxia wineries, an emerging wine tourism destination in China.

This study uses social network analysis to uncover “what” the spatial structure of wine tourism networks looks like. Sixteen in-depth interviews were conducted among key stakeholders to explain the “why” of such structural characteristics.

The results show that in an emerging wine tourism destination, popular tourist attractions enjoy high centrality and hold key positions in the wine tourism network. Small wineries exhibit high closeness centrality, and only one winery serves as a network broker. According to the stakeholders, the importance of network actors will increase as their economic and political importance increase, while small wineries that lack differentiation in the network may perish.

Local governments can implement the suggested measures for improving network connections, and wineries are advised to find suitable positions to improve the experiences of tourists.

This study pioneers the identification of the distinct structure and factors influencing the network of an emerging wine tourism destination, thus enriching the understanding of the interplay and roles of different actors.

The need for stakeholder theory has been widely highlighted in the literature to develop solid strategies for a large organization. However, there is still a lack of user-friendly visualization tools and no unique approach exists to identify and engage stakeholders. This paper aims to propose a general methodology to co-design the sustainability ecosystem at the local scale, to explore it and to assess the impact of a large organization within the identified ecosystem.

The methodology consists of two main processes: identifying an ontological map of the sustainability topics network and designing the local sustainability stakeholders ecosystem. Both processes are based on a nodes identification phase and a nodes prioritization phase. The identification phase was achieved by engaging 160 citizens, for the topics network and nearly 40 relevant stakeholders, for the stakeholders’ ecosystem, with a collaborative participatory mapping process. The prioritization phase was conducted because of three indicators, i.e. the closeness, the betweenness and the eigenvector centrality.

Betweenness centrality results to be the best indicator to assess the importance of a stakeholder with respect to the whole network, while eigenvector centrality highlights the quality of the already engaged stakeholders of an organization, as it mainly depends on the number of links of the first order neighbors. On the contrary, the closeness centrality, when applied to a small network, seems to be not appropriate to assess the centrality of a stakeholder.

This approach revealed some criticalities in the mapping process, as in the weighting link procedure. Further investigations are needed to generalize the approach to a dynamic one, to allow real-time mapping and to develop a robust interconnection among centrality degrees and the power, interest and legitimacy concept of stakeholder theory.

Obtained results for a case study, i.e. the position of the University of Turin Green Office within the City of Turin sustainability ecosystem, are discussed showing how social network analysis centrality degrees can be used to quantitatively assess the role of an organization within a stakeholders’ ecosystem.

Centrality analysis allows identifying emergent topics/stakeholders within a network of words/actors that, at a first sight, should not be considered by decision-makers and managers.

A new methodology for stakeholder identification and prioritization is proposed exploiting online data visualization tools, participatory mapping and social network analysis.

This study investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on the Asian-Australasian cruise shipping network. The analysis was carried out using complex network analysis and data collected for two periods, before and after the pandemic outbreak. The analysis revealed that the network structure and properties have changed after the outbreak of the COVID-19 pandemic. Interestingly, the network’s density and the number of links have increased, but its scale-free property remains with the degree distribution follows the power law. The network has a higher connectivity efficiency with a smaller average path length and a higher clustering coefficient. Its hub ports still maintain an extensive connection. The network’s flow efficiency becomes higher and connectivity stronger after the pandemic. The role of cruise ports has changed as indicated by the degree, betweenness, closeness and eigenvector centralities. The study’s findings indicate that the cruise shipping sector could further enhance efficiency and identify strategies to assist the management in similar circumstances.

In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features.

This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network.

Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs.

The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy.

The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.