A new measure of identifying influential nodes: Efficiency centrality

Sha-sha, Wang; Du, Yuxian; Deng, Yong

doi:10.1016/j.cnsns.2016.11.008

Cited by 127 publications

(54 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The importance of notions and facts needs to be approached from a more holistic perspective, by considering how given facts and notions (in what follows referred to as elements of the network) are linked as part of the network, and how they affect other elements in the network not directly connected to the given element. The research problem is approached by using network cartographic methods (Börner 2015;Börner and Scharnhorst 2009;Chen et al 2009;Shi et al 2015) based on degree and Katz centralities (Estrada 2012;Borgatti 2005;Katz 1953;Sharkey 2017;Wang et al 2017) and modularity optimisation (Newman and Girvan 2004;Newman 2004). These methods provide complementary information on the skeletal structure of the network, how different thematic traits are connected to different landmark nodes (high affinity nodes), and how those distributions of centralities evolve as the network expands when the temporal window on historic periods is changed.…”

Section: Methodsmentioning

confidence: 99%

“…Katz centrality is therefore suitable for our purposes to explore the global connectivity of the network and which nodes are the most important ones in contributing to global connectivity. In addition to Katz centrality, we also use Katz centrality efficiency (Katz efficiency in what follows), which is defined through the effect of isolation of the node (Sharkey 2017;Wang et al 2017).…”

Section: Finding Key Nodes: the Landmarksmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying the impacts of expanding social protection on efficiency and equity: Evidence from a behavioral microsimulation model for Ghana

Osei

Pirttilä

Rattenhuber

2017

WIDER Working Paper

View full text Add to dashboard Cite

We examine students' representations of their conceptions of the interlinked nature of science history and general history, as well as cultural history. Such knowledge landscapes of the history of science are explored by using the knowledge cartographic, network-based method of analysis to reveal the key items, landmarks, of the landscapes. We show that Katz centrality and Katz centrality efficiency are robust and reliable measures for finding landmarks. It is shown that landmarks are most often persons but include also colligatory landmarks, which refer to broader sets of events or ideas. By using Katz centrality we study how landmarks depend on periodisation of the networks to see what kinds of changes occur by changing the time window on history. The community structure of the networks is studied by using the Louvain method, to reveal the strong thematic dependence of the communities. When landmarks are studied in relation to community structure, it is found that colligatory landmarks gain importance in relation to person-centred landmarks. Network-based cartography thus reveals many features about landmarks, how communities emerge around them and how they depend on periodisation, which traditional methods can only detect or identify with difficulty. Such knowledge has direct impact on the design and planning of education and courses which could better address the need to facilitate a deeper understanding of the related nature of science history and history in general.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Finding Key Nodes: the Landmarksmentioning

confidence: 99%

Quantifying the impacts of expanding social protection on efficiency and equity: Evidence from a behavioral microsimulation model for Ghana

Osei

Pirttilä

Rattenhuber

2017

WIDER Working Paper

View full text Add to dashboard Cite

show abstract

“…The degree centrality of a public transport facility is the number of connected facilities [36]. In the complex transport system, the number of traffic routes connected to the station, population or activities covered by stations, or potential traffic flow between stations can be factors for measuring connections between nodes [37,38]. According to Tobler's first law of geography, near things are more related to each other [39,40].…”

Section: Related Workmentioning

confidence: 99%

Comprehensive Public Transport Service Accessibility Index—A New Approach Based on Degree Centrality and Gravity Model

Yang

Liu

Gan³

2019

Sustainability

View full text Add to dashboard Cite

: Public transport accessibility (PTA) is an essential index for evaluating the efficiency of urban public transport resource and public service. Improving public transport accessibility is considered as the most effective way of alleviating urban congestion and promoting urban sustainability. PTA can be divided into three types, which are access to stations, accessibility of networks and access to activities. This paper focuses on evaluating access to public transport service at stations, considering walking time to stations and waiting time for services at stations. Numerous studies have been carried out on evaluating the accessibility of public transport stations. When building accessibility evaluation model, rare has seen different public transport modes as an integrated system. Hence the topological structure and geometrical layout of the system are not considered. In this paper, factors like the configuration of the public transport system and the surrounding environment of stations are included for the evaluation. The centrality of station index (COS) is presented to describe the importance of stations in the integrated public transport system. The COS index is an improved combination of the gravity model and degree centrality index of the complex network. This index improves the degree centrality index by replacing the number of nodes with weighted connections between stations. By modeling public transport operation, configuration and surroundings of stations, a comprehensive public transport service accessibility index (CPTAI) is formulated to quantify accessibility at the community level. To compute this index, a network analysis model is firstly applied to find the nearest station for each point of interest (POI) by using ArcGIS desktop 10.2, and the transport service frequency at the nearest station is measured. Then Baidu Map API is employed to measure the impedance indexes between stations in the integrated public transport network. Activities covered by stations within a given distance are seen as the generation and attraction of trips in between the stations. Then a weighted gravity model and COS is presented to calculate the integrated service frequency (ISF) for each POI afterward. In the end, the index is converted to the community level, which is CPTAI. The experiment is carried out in Wuhan metropolitan area, Hubei, China. Smart card data (SCD) is utilized to evaluate CPTAI and examine the association between commuting trips by public transport and accessibility level within Wuhan metropolitan area. Experimental results show that CPTAI has a significant statistical association with trips by public transport.

show abstract

“…Based on this, identifying influential nodes in complex network has been focus of the research of complex networks, which can control the spread of disease [19] and identify the most influential spearders [20]. A great deal of centrality measures of identifying the influential nodes have been proposed in the past years [21,22]. The first person to identify the influential nodes is Shimbel [23] in 1953, and the method is that the node's centrality is the number of shortest paths which go through the selected node.…”

Section: Introductionmentioning

confidence: 99%

Identifying influential nodes based on fuzzy local dimension in complex networks

Wen

Jiang

2019

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

How to identify influential nodes in complex networks is an important aspect in the study of complex network. In this paper, a novel fuzzy local dimension (FLD) is proposed to rank the influential nodes in complex networks, where a node with high fuzzy local dimension has high influential ability. This proposed method focuses on the influence of the distance from the center node on the local dimension of center node by fuzzy set, resulting in a change in influential ability. In order to show this proposed method's effectiveness and accuracy, four real-world networks are applied in this paper. Meanwhile, Susceptible-Infected (SI) is used to simulate the spreading process by FLD and other centrality measures, and Kendall's tau coefficient is used to describe the correlation between the influential nodes obtained by centrality and the results measured by SI model. Observing from the ranking lists and simulated results, this method is effective and accurate to rank the influential nodes.

show abstract

A new measure of identifying influential nodes: Efficiency centrality

Cited by 127 publications

References 48 publications

Quantifying the impacts of expanding social protection on efficiency and equity: Evidence from a behavioral microsimulation model for Ghana

Quantifying the impacts of expanding social protection on efficiency and equity: Evidence from a behavioral microsimulation model for Ghana

Comprehensive Public Transport Service Accessibility Index—A New Approach Based on Degree Centrality and Gravity Model

Identifying influential nodes based on fuzzy local dimension in complex networks

Contact Info

Product

Resources

About