Density centrality: identifying influential nodes based on area density formula

Ibnoulouafi, Ahmed; Haziti, Mohamed El

doi:10.1016/j.chaos.2018.06.022

Cited by 39 publications

(27 citation statements)

References 30 publications

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“…Brief definitions and illustrations of the measures are summarized in Table 2 and Figure 2. A thorough description of these metrics can be consulted in, for example, [20,38,39]. Table 2.…”

Section: Street Network Measuresmentioning

confidence: 99%

Analysis of Urban Drivable and Walkable Street Networks of the ASEAN Smart Cities Network

Zhao

Yen

Bailey

et al. 2019

IJGI

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Making transport systems sustainable is a topic that has attracted the attention of many researchers and urban planners. The Association of Southeast Asian Nations (ASEAN) Smart Cities Network (ASCN) was initiated to develop a sustainable transport system in the ASEAN countries. A comprehensive understanding of street networks (SNs) can contribute significantly to the achievement of this initiative. Therefore, this paper measured and compared characteristics of drivable street networks (DSNs) and walkable street networks (WSNs) of the 26 ASCN pilot cities by applying multiple network metrics. The OSMnx tool was used to download and analyse WSNs and DSNs from the OpenStreetMap. The findings present the topological and geometric characteristics of WSNs and DSNs that are diverse and characterized by different factors. The cities with orthogonal street grids, high street density, intersection density, and fewer cul-de-sacs have good accessibility to reach destinations. In contrast, some other cities have more curvilinear and circuitous SNs with many missing links to other streets, which in turn are prone to traffic disruption. The study highlights the important features of SNs that have significant implications for future designs of SNs in the ASCN whose goal is to make cities smart and liveable for ASEAN members.

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“…Brief definitions and illustrations of the measures are summarized in Table 2 and Figure 2. A thorough description of these metrics can be consulted in, for example, [20,38,39]. Table 2.…”

Section: Street Network Measuresmentioning

confidence: 99%

Analysis of Urban Drivable and Walkable Street Networks of the ASEAN Smart Cities Network

Zhao

Yen

Bailey

et al. 2019

IJGI

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“…A network can be denoted by G, equated as G = (V, E), where V and E represent nodes and edges, respectively. Moreover, Betweenness Centrality (BC) 32 , Closeness Centrality (CC) 33 , HITS 34 , H-Index (HI) 35 , Profit Leader (PL) 28 , Improved k-shell hybrid method (IKSh) 12 , Extended Cluster Coefficient Ranking Measure (ECRM) 36 , Gravity Index Centrality (GIC) 30 and Density centrality (DNC) 37 are introduced in this section. Betweenness Centrality (BC): BC calculates influential nodes based on global information 32 .…”

Section: Preliminariesmentioning

confidence: 99%

Identification of nodes influence based on global structure model in complex networks

Ullah

Wang

Sheng

et al. 2021

Sci Rep

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Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s).

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“…SNs metrics are defined in some studies (Boeing, 2017; Ibnoulouafi and El Haziti, 2018; Kirkley et al., 2018). Some definitions of the metrics associated with SNs are herewith presented.…”

Section: Literature Reviewmentioning

confidence: 99%

The morphology and circuity of walkable, bikeable, and drivable street networks in Phnom Penh, Cambodia

Yen

Zhao

Sohail

2019

Environment and Planning B: Urban Analytics and City Science

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Street network analysis is a growing area in sustainable transportation research. Most academic papers on the topic have, so far, been concentrated in Europe and America, with less attention paid to rapidly growing cities in low income nations. This is problematic because transportation networks are rapidly evolving in developing countries and the impacts of misguided transportation policies (including air pollution and road traffic casualties) are particularly acute. Metrics on the performance of street networks could help inform policy. This paper uses the Python package OSMnx to analyze and evaluate street networks in 12 districts of Phnom Penh from OpenStreetMap. Results suggest that topological and geometric characteristics of street networks are more conducive to walking and biking in the central districts than in the peripheral districts. The central districts are also better connected to core network corridors. To promote sustainable urban mobility, new developments and street renewals should be incorporated facilities, services, and safety of walking and biking. Some policy implications are suggested for future designs of the Phnom Penh’s street networks to increase livability and sustainability.

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Density centrality: identifying influential nodes based on area density formula

Cited by 39 publications

References 30 publications

Analysis of Urban Drivable and Walkable Street Networks of the ASEAN Smart Cities Network

Analysis of Urban Drivable and Walkable Street Networks of the ASEAN Smart Cities Network

Identification of nodes influence based on global structure model in complex networks

The morphology and circuity of walkable, bikeable, and drivable street networks in Phnom Penh, Cambodia

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