Topological analysis in scientific social networks to identify influential researchers

Guércio, Hugo; Ströele, Victor; David, José; Braga, Regina; Campos, Fernanda

doi:10.1109/cscwd.2017.8066709

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…Among the many complex networks, cooperative networks are popular among researchers, due to their universality in the real world, such as corporate cooperation networks, industry-university-research cooperation networks, actor cooperation networks, scientific research cooperation networks, etc. [45]. A lot of research has been done on network model construction and feature analysis.…”

Section: Description Of Vehicle Assistance Network Evolution Model Bamentioning

confidence: 99%

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

Ran

Bai

2020

Sensors

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In this paper, we propose an optimization framework of vehicular fog computing and a cooperation vehicular network model. We aim to improve the performance of vehicular fog computing and solve the problem that the data of the vehicle collaborative network is difficult to obtain. This paper applies the hypergraph theory to study the underlying structure, considering the social characteristics of the vehicles and vehicle communication. Since the vehicles join the network in accordance with the Poisson process law, the model is analyzed by using Poisson stochastic process and mean field theory. This paper uses MATLAB to simulate the evolution process of cooperative networks. The results show that the vehicle's super-degree in vehicular fog computing has scale-free characteristics. Through this model, the vehicle cooperation situation can be analyzed, and the vehicle dynamics can be accurately predicted to further improve the performance of vehicular fog computing. The model can be transformed into some complex network models by adjusting the parameters. It has strong universality and has certain reference significance for the research on the related characteristics of VANETs and the theoretical research of the cooperative network.2 of 17 to higher network delays. For vehicles that are using autonomous driving in the Internet of Vehicles, high latency is likely to cause serious traffic accidents. Cellular network can provide limited enhanced communication, mainly controlled by network operators, which is inefficient from the application point of view [8]. Mobile cloud computing can bring rich computing resources to mobile users [9], but it is costly and time consuming to upload real-time information [10].Fog computing can effectively solve the above problems, by deploying a fog server with storage and computing near the user to receive and process user data [11]. The deployment location of the fog server is closer to the user, which can save a lot of data transmission time. The deployment of fog computing nodes in the connected vehicle network has become a popular trend in the research of VANETs. We finally proposed a novel concept of vehicular fog computing (VFC), which uses vehicles as infrastructure to make full use of vehicular communication and computing resources. Specifically, VFC is an architecture that has cloud-based features and provides data, computing, storage and application services to end users. VFC is also differentiated from other existing technologies by being close to end users, and having dense geographical distribution and support for mobility; its bottom layer consists of cooperative vehicles, so that the vehicle is both a fog computing provider and a vehicular fog computing user. It can use computing resources nearby and reduce the use of communication resources. In the fog computing architecture, vehicles and mobile devices are end users, and there is a fog server layer between these edge users and the cloud. The low-level equipment of vehicular fog computing vehicles has high-speed moving ch...

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Section: Description Of Vehicle Assistance Network Evolution Model Bamentioning

confidence: 99%

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

Ran

Bai

2020

Sensors

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“…Their study found that densification occurred as the size of networks emerged from the co-authorships increased over time. Apart from centrality measures, Guércio et al [13] described how the topology analysis can be applied with centrality values in identifying the influential node. Impact of the loss of an influential node is verified by node removals [13].…”

Section: Node Centralitymentioning

confidence: 99%

“…Apart from centrality measures, Guércio et al [13] described how the topology analysis can be applied with centrality values in identifying the influential node. Impact of the loss of an influential node is verified by node removals [13]. Basically, in social network analysis, high degree node is likely more influential in the network.…”

Section: Node Centralitymentioning

confidence: 99%

Exploratory Experiment on Co-Authorship Network using Social Network Analysis Metrics and Measures

Magalingam¹,

Samy²,

Maarop³

et al. 2018

IJET

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This paper contributes in understanding and gaining meaningful insight about the relationship among the scientist in the co-authorship network using social network analysis. We argue that the relationship analysis is not always a straightforward process. In the past one single measure, for example, the egocentric or centrality measure was used to describe the scientific collaboration patterns separately. In this paper, various analysis such as centrality analysis, ego network, community detection, largest clique and word frequency have been used to examine and interpret the collaboration among the authors. This research is not dominated by known researchers but involves an overall exploration of the network. Our research is mainly guided by the creation of research issues, assessing the type of dataset and the objectives for presenting the co-authorship relationships. It is important to identify the motive of the selected measures in order to achieve the predefined objective. Specific methodology and procedures are designed to solve each research issue respectively. This study reveals that the network interpretation should not be solely based on one network measure, but an explorative analysis results need to be considered because it allows exploring the hidden information through the changes in the network structure, topology patterns and nodes’ position.

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Topological analysis in scientific social networks to identify influential researchers

Cited by 2 publications

References 29 publications

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

Exploratory Experiment on Co-Authorship Network using Social Network Analysis Metrics and Measures

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