Robustness analysis metrics for worldwide airport network: A comprehensive study

Sun, Xiaoqian; Gollnick, Volker; Wandelt, Sebastian

doi:10.1016/j.cja.2017.01.010

Cited by 86 publications

(45 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate the effectiveness of the DSHC method, six real complex networks including Facebook (Slavo Zitnik's social network on Facebook) [39], Erdos (scientific collaboration network) [40], USAir (American aviation network) [41,42], USAirport (USA airport network) [43], Yeast (yeast protein-protein binding network) [44,45], and Power (connections between US west power stations) [46] are used for a comparison experiment. The basic statistics of six real networks are shown in Table 1.…”

Section: Data Descriptionmentioning

confidence: 99%

Critical Nodes Identification in Complex Networks

Yang

2020

Symmetry

View full text Add to dashboard Cite

Critical nodes identification in complex networks is significance for studying the survivability and robustness of networks. The previous studies on structural hole theory uncovered that structural holes are gaps between a group of indirectly connected nodes and intermediaries that fill the holes and serve as brokers for information exchange. In this paper, we leverage the property of structural hole to design a heuristic algorithm based on local information of the network topology to identify node importance in undirected and unweighted network, whose adjacency matrix is symmetric. In the algorithm, a node with a larger degree and greater number of structural holes associated with it, achieves a higher importance ranking. Six real networks are used as test data. The experimental results show that the proposed method not only has low computational complexity, but also outperforms degree centrality, k-shell method, mapping entropy centrality, the collective influence algorithm, DDN algorithm that based on node degree and their neighbors, and random ranking method in identifying node importance for network connectivity in complex networks.

show abstract

Section: Data Descriptionmentioning

confidence: 99%

Critical Nodes Identification in Complex Networks

Yang

2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the proposed method, we apply it to real and synthetic networks. The real networks include: USAir (American aviation network) [43,44], Netscience (Scientist cooperation network) [45,46], Infectious (People infection network) [47,48], USAirport (American airport network) [49,50], Yeast (Protein interaction network) [51,52] and Power (Power grid of the western United States) [2,39]. The statistical properties of the six networks are presented in Table 1.…”

Section: Data Descriptionmentioning

confidence: 99%

Identifying Node Importance in a Complex Network Based on Node Bridging Feature

Jiang

Jing

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

show abstract

“…Because of the urban PTN as the core component of urban integrated transportation network and a similar analytical framework existing in various transportation networks, the complexity and resilience of urban PTN are reviewed in the following text, while the complexity and resilience of other transportation networks can be seen in the pioneer reviews of Reggiani et al [19,26] and Mattsson et al [18]. The complexity and resilience of air transportation network especially should be paid more attention, such as the pioneer review of Zanin et al [16] and the related studies of Neal et al [15], Wandelt et al [27], Sun et al [28,29], and Cardillo et al [30]. As a result, the key features of urban PTN based on complex network theory can be obtained through focusing on perspective of engineering applicability; especially they are extracted from numerous transportation network studies.…”

Section: Introductionmentioning

confidence: 99%

A Review and Prospect for the Complexity and Resilience of Urban Public Transit Network Based on Complex Network Theory

Zhang

Lü

et al. 2018

Complexity

View full text Add to dashboard Cite

The complexity and resilience of urban public transit network (PTN) are the interdisciplinary study area between transportation engineering and system science, which is a good demonstration of applying complex network theory to promote the development of engineering science. The deep understanding of this study helps to provide a new perspective for analyzing the reliability of urban PTN. Following study process of the complexity and resilience of complex network, this paper reviews the complexity and resilience of PTN from four topics, i.e., the PTN complexity, the static resilience of PTN, the dynamic resilience (cascading failures based resilience) of single layered PTN, and the dynamic resilience of interdependent PTN. In the literature review, multiple key items are, respectively, extracted for each topic, and the engineering applicability of each topic is also analyzed, which are both for obtaining the key features of this study area. Finally, in order to realize the development trend of cyclic and forward—complex network theory, network resilience theory, transforming into a realistic model and method that is close to actual public transit operation, engineering application and practice, and contributing to complex network theory, the study status is summarized and the future development trend is prospected.

show abstract

Robustness analysis metrics for worldwide airport network: A comprehensive study

Cited by 86 publications

References 42 publications

Critical Nodes Identification in Complex Networks

Critical Nodes Identification in Complex Networks

Identifying Node Importance in a Complex Network Based on Node Bridging Feature

A Review and Prospect for the Complexity and Resilience of Urban Public Transit Network Based on Complex Network Theory

Contact Info

Product

Resources

About