Breakup of Directed Multipartite Networks

Cai, Qing; Pratama, Mahardhika; Alam, Sameer; Ma, Chunyao; Liu, Jiming

doi:10.1109/tnse.2019.2894142

Cited by 7 publications

(8 citation statements)

References 83 publications

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“…Model 1 is in line with the models used by existing studies in Ref. [37,38]. Model 2—Directed network scenario.…”

Section: Research Problem and Methodologymentioning

confidence: 80%

“…For a given undirected network G, we detach all the edges previously connected to the q fraction of nodes of network G. Model 1 is in line with the models used by existing studies in Ref. [37,38]. Model 2-Directed network scenario.…”

Section: Cascading Failure Modelmentioning

confidence: 99%

“…To quantify the resilience of airport networks under node failures, we introduce four metrics based on existing literature. [37][38][39][40] Here we denote the four metrics respectively as Rn_GC, Rn_RC, Rm_GC and Rm_RC. Given a network G with n nodes and m edges, we assume that q fraction of its nodes are failed.…”

Section: Resilience Metricsmentioning

confidence: 99%

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Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

Xiao

Gao

et al. 2022

Int J of Intelligent Sys

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Air traffic systems are of great significance to our society. However, air traffic systems are extremely complicated since an air traffic system encompasses many components which could evolve over time. It is therefore challenging to analyze the evolution dynamics of air traffic systems. In this paper we propose a graph perspective to trace the spatial-temporal evolutions of air traffic systems. Different to existing studies which are model-driven and only focus on certain properties of an air traffic system, in this paper we propose a data-driven perspective and analyze a couple of properties of an air traffic system. Specifically, we model air traffic systems with both unweighted and weighted graphs with respect to real-world traffic data. We then analyze the evolution dynamics of the constructed graphs in terms of nodal degrees, degree distributions, traffic delays, causality between graph structures and traffic delays, and system resilience under airport failures. To validate the effectiveness of the proposed approach, a case study on the American air traffic systems with respect to 12-month traffic data is carried out. It is found that the structures and traffic mobilities of the American air traffic systems do not evolve significantly over time, which leads to the stable distributions of the traffic delays as evidenced by a causality analysis. It is further found that the American

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“…Model 1 is in line with the models used by existing studies in Ref. [37,38]. Model 2—Directed network scenario.…”

Section: Research Problem and Methodologymentioning

confidence: 80%

Section: Cascading Failure Modelmentioning

confidence: 99%

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Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

Xiao

Gao

et al. 2022

Int J of Intelligent Sys

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“…Suppose that 1 − p fraction of network components are disconnected to the rest of the focal network due to external/internal perturbations. The disconnection of those failed network components can fragment the focal network into pieces amongst which there exists the largest connected component (LCC) [38]. The LCC of a network is an essential indicator for capturing the network's capability in response to perturbations.…”

Section: Percolation Theorymentioning

confidence: 99%

Critical Links Detection in Spatial-Temporal Airway Networks Using Complex Network Theories

Alam

Cai

et al. 2022

IEEE Access

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In an airway network, some critical links exist that are vital for the structural integrity and performance of the network. The detection of such links may assist with improving the imbalance between the limited airspace capacity and the ever-increasing traffic demand, which elicit flight delays, significant economic losses, etc. However, it is challenging to identify such links as they evolve (both in space and time) with changing traffic flow dynamics. This paper proposes a complex network approach for spatial-temporal critical links detection in a given airway network. First, flight track data is employed to characterize the airway network as weighted spatial-temporal networks. Then edge centrality and network percolation metrics are adopted to detect the critical links in each snapshot of the spatial-temporal networks. Afterward, the critical links detected by the two metrics are spatially overlapped to determine the final critical links over time. To examine the operational validity of the proposed method, we carry out a case study on the Southeast Asia airway network derived from one-month flight track data. Results demonstrate that the spatial distribution of the critical links varies over different traffic scenarios, and most of the identified critical links are found in the transition sectors with complex traffic situations. Four links, which are parts and at crosses of major trunk airways connecting to major navigation aids (VOR/DME) in the studied network, appear highly in all examined traffic scenarios. The unavailability of such links may lead to traffic flow disruptions. Observations by subject matter experts from air-traffic data visualizations demonstrate that the complex network-based methods can dynamically identify airway links that are operationally critical under time-evolving air-traffic scenarios. With good traffic flow prediction tools in the future, this method can be adopted to predict critical links in airway networks to better assist controllers in real-time air traffic management.

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“…To describe the product supply-demand relations between manufacturers and suppliers, multipartite network is used to model this supply network. Multipartite network or multipartite graph is an important part of complex networks, which is characterized by the existing of different types of nodes and edges only exist between different types of nodes [36].…”

mentioning

confidence: 99%

Memetic Algorithm With Local Neighborhood Search for Bottleneck Supplier Identification in Supply Networks

2020

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With the development of lean manufacturing and economic globalization, supply networks increasingly become complex and large-scale, within which thousands of firms inter-depend with each other. Due to these increasing inter-dependencies, disruption of a quite few critical suppliers, namely bottleneck suppliers, can induce high loss to a supply network and even make the whole network dysfunction. Identification of bottleneck suppliers is significantly important for supply network risk management. Thus, in this paper, a method based on a memetic algorithm with local neighborhood search (MALNS) is proposed to identify bottleneck suppliers in a two-stage supply network. Firstly, a model based on multipartite network is designed to describe the product supply-demand relations between multiple manufacturers and suppliers, which considers the different roles of manufacturer and supplier and differentiates the products that suppliers supply. To assess the loss caused by supplier disruptions, two performance metrics of supply networks, average product availability rate and manufacturer functioning rate, are presented. Then, a MALNS-based method is proposed to identify bottleneck suppliers, i.e., suppliers whose disruption will decrease both performance metrics most greatly. Finally, a case study based on a real automobile supply network is presented to validate the applicability and effectiveness of the proposed method.

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Breakup of Directed Multipartite Networks

Cited by 7 publications

References 83 publications

Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

Critical Links Detection in Spatial-Temporal Airway Networks Using Complex Network Theories

Memetic Algorithm With Local Neighborhood Search for Bottleneck Supplier Identification in Supply Networks

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