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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Temporal Event Graph Approach and Robustness Analysis for Air Transport Networ

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Author(s):
Sano, Humberto Hayashi [1] ; Berton, Lilian [1]
Total Authors: 2
Affiliation:
[1] Univ Fed Sao Paulo, Inst Sci & Technol, BR-12247014 Sao Paulo, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING; v. 8, n. 4, p. 3453-3464, OCT 1 2021.
Web of Science Citations: 0
Abstract

Air transport networks (ATNs) are critical to mobility and the worldwide economy. ATNs are temporal networks characterized by time-stamped information and a sequence of recurrent temporal events. Thus, static analyses disregard the real-world temporal structure of ATN. The trips are driven by considerations of cost and convenience, this way, more accurate representations, and models are necessary. Despite that, few studies have explored temporal representation for ATN. Here, we propose a temporal representation for ATN using event graphs, investigating the temporal network's topology and robustness. We also introduce a new Temporal Breadth-First-Search Pruning (T-BFS-P) algorithm to traverse the event graph paths. A study in the U.S. and Brazilian domestic ATN's robustness is presented, in which target attacks by centrality measures, giant component variation, and the robustness measure were conducted. The results show that our T-BFS-P is five times faster in execution than Dijkstra and classical BFS for the U.S. ATN. It also allows finding the shortest paths with the minimum number of nodes (airports) in the path, optimizing travelers' connections. This work contributes to temporal network area reporting a new representation and temporal BFS algorithm via event graph, and a pioneer study to analyze robustness in event graph. (AU)

FAPESP's process: 18/01722-3 - Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications
Grantee:Lilian Berton
Support Opportunities: Regular Research Grants