Centrality Measures in Complex Networks: A Survey

Saxena, Akrati; Iyengar, S. R. S.

doi:10.48550/arxiv.2011.07190

Cited by 18 publications

(24 citation statements)

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“…We use an encoder-decoder-based U-net architecture 26 as a segmentation model, tailoring it for our application. The architecture is composed by a cascade of [16,32,64,128,256] convolutional filters activated by a relu activation function, followed by a batch normalization layer and a Max Pooling layer. The architecture is shown in Fig.…”

Section: Roadway Datamentioning

confidence: 99%

Traffic monitoring system design considering multi-hazard disaster risks

Gazzea

Miraki

Alisan

et al. 2023

Sci Rep

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Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes. At the same time, traffic flow data are a fundamental type of information for any transportation system. In this paper, we tackle the problem of traffic sensor placement on roadways to address two tasks at the same time. The first task is traffic data estimation in ordinary situations, which is vital for traffic monitoring and city planning. We design a graph-based method to estimate traffic flow on roads where sensors are not present. The second one is enhanced observability of roadways in case of extreme weather events. We propose a satellite-based multi-domain risk assessment to locate roads at high risk of closures. Vegetation and flood hazards are taken into account. We formalize the problem as a search method over the network to suggest the minimum number and location of traffic sensors to place while maximizing the traffic estimation capabilities and observability of the risky areas of a city.

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Section: Roadway Datamentioning

confidence: 99%

Traffic monitoring system design considering multi-hazard disaster risks

Gazzea

Miraki

Alisan

et al. 2023

Sci Rep

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“…• Coreness: It represents how well a node is connected to other important nodes and also with periphery nodes in the network (Saxena & Iyengar, 2020). A k-core of 𝑘 indicates that a node is connected to a subset of nodes that have at least 𝑘 degree or higher.…”

Section: Co-authorship Networkmentioning

confidence: 99%

The many facets of academic mobility and its impact on scholars' career

Momeni

Karimi²,

Mayr³

et al. 2022

Journal of Informetrics

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“…There are excellent introductions to temporal graphs that include surveys on different temporal centrality measures, see, e.g., [16,25,47]. Further overviews of centralities and their applications are provided, e.g., in [8,10,24,44,45,48,56]. In a recent work, Buß et al [5] discuss several variants of temporal betweenness based on shortest paths and study their theoretical complexity and practical hardness.…”

Section: Related Workmentioning

confidence: 99%

“…The goal is to determine the importance of nodes in the network and find the most central ones. Various concepts of centrality have been proposed [33,48], and their informative value must be assessed based on a research question. A prime example is the PageRank algorithm [40] for ranking web pages in search engine results by performing random walks among them.…”

Section: Introductionmentioning

confidence: 99%

Temporal Walk Centrality: Ranking Nodes in Evolving Networks

Oettershagen¹,

Mutzel²,

Kriege³

2022

Preprint

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We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network. In contrast to the widely-used betweenness centrality, we assume that information does not necessarily spread on shortest paths but on temporal random walks that satisfy the time constraints of the network. We show that temporal walk centrality can identify nodes playing central roles in dissemination processes that might not be detected by related betweenness concepts and other common static and temporal centrality measures. We propose exact and approximation algorithms with different running times depending on the properties of the temporal network and parameters of our new centrality measure. A technical contribution is a general approach to lift existing algebraic methods for counting walks in static networks to temporal networks. Our experiments on real-world temporal networks show the efficiency and accuracy of our algorithms. Finally, we demonstrate that the rankings by temporal walk centrality often differ significantly from those of other state-of-the-art temporal centralities. CCS CONCEPTS• Information systems → Social networks; • Theory of computation → Graph algorithms analysis.

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Centrality Measures in Complex Networks: A Survey

Cited by 18 publications

References 0 publications

Traffic monitoring system design considering multi-hazard disaster risks

Traffic monitoring system design considering multi-hazard disaster risks

The many facets of academic mobility and its impact on scholars' career

Temporal Walk Centrality: Ranking Nodes in Evolving Networks

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