Social Network Analysis: Hardly easy

Srivastava, Atul; Anuradha, .; Gupta, Dimple Juneja

doi:10.1109/icroit.2014.6798311

Cited by 15 publications

(8 citation statements)

References 23 publications

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“…In order to better understand the behavior of social-based vehicular ad hoc networks, we need to refer to main tools of Social Network Analysis (SNA) [54]. SNA takes into account social relationships in terms of nodes (i.e., individuals) and ties (i.e., relationships among nodes), and identifies important components in a social network, such as the centrality metrics that are used to denote how "important" a node is inside a network.…”

Section: A Content Dissemination In Vsnsmentioning

confidence: 99%

A Survey on Vehicular Social Networks

Vegni

Loscrì

2015

IEEE Commun. Surv. Tutorials

239

103

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This paper surveys recent literature on vehicular social networks that are a particular class of vehicular ad hoc networks, characterized by social aspects and features. Starting from this pillar, we investigate perspectives on next-generation vehicles under the assumption of social networking for vehicular applications (i.e., safety and entertainment applications). This paper plays a role as a starting point about socially inspired vehicles and mainly related applications, as well as communication techniques. Vehicular communications can be considered the "first social network for automobiles" since each driver can share data with other neighbors. For instance, heavy traffic is a common occurrence in some areas on the roads (e.g., at intersections, taxi loading/unloading areas, and so on); as a consequence, roads become a popular social place for vehicles to connect to each other. Human factors are then involved in vehicular ad hoc networks, not only due to the safety-related applications but also for entertainment purposes. Social characteristics and human behavior largely impact on vehicular ad hoc networks, and this arises to the vehicular social networks, which are formed when vehicles (individuals) "socialize" and share common interests. In this paper, we provide a survey on main features of vehicular social networks, from novel emerging technologies to social aspects used for mobile applications, as well as main issues and challenges. Vehicular social networks are described as decentralized opportunistic communication networks formed among vehicles. They exploit mobility aspects, and basics of traditional social networks, in order to create novel approaches of message exchange through the detection of dynamic social structures. An overview of the main state-of-the-art on safety and entertainment applications relying on social networking solutions is also provided

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Section: A Content Dissemination In Vsnsmentioning

confidence: 99%

A Survey on Vehicular Social Networks

Vegni

Loscrì

2015

IEEE Commun. Surv. Tutorials

239

103

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“…Fig. 7 depicts the SCARF forwarding probability for the ith vehicle at distance d = 150 m within the source vehicle's transmission range (i.e., z = 200 m, which can be regarded as a possible range of an IEEE 802.11p device) versus the social degree S i and for different values of c i (i.e., c i = [1,2,5]). The shape factor implicitly takes into account information about the quality of channel, acting as the signal-to-noise ratio associated to the communication channel of the i-th vehicle.…”

Section: Scarf Techniquementioning

confidence: 99%

“…where a min represents the minimum value of the activity degree a, and α is the exponent of the power law distribution, which is in the range [2,3] for small-world networks [48]. Substituting Eq.…”

Section: A Analytical Performance Evaluationmentioning

confidence: 99%

“…In order to better understand the behaviour of social-based vehicular networks, many researchers make use of the main tools of Social Network Analysis (SNA) [2], which takes into account social relationships in terms of nodes (i.e., individuals) and ties (i.e., relationships among nodes), to identify the important components of a social network [3]. For example, Xia et al [4] describe BEEINFO, an interest-based forwarding mechanism that uses an artificial bee colony for sociallyaware networking, that is, a routing mechanism that classifies communities into specified categories on the basis of personal interests.…”

Section: Introductionmentioning

confidence: 99%

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Data Transmissions Using Hub Nodes in Vehicular Social Networks

Vegni

Souza

Loscrì

et al. 2020

IEEE Trans. on Mobile Comput.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…They are very important and most commonly used in the fields of SNA. So, they are considered the fundamental properties of social structures and individuals, which represent the power even if there is dispute or inconsistencies about their definitions and consequences [26] [27]. Having more opportunities and alternatives, such as a 'connector' or 'hub' in the network.…”

Section: Social Network Analysismentioning

confidence: 99%

Considering Stakeholders’ Feedback in Requirements Prioritization using Social Network Analysis

Hamad¹,

El‐Sayed²,

Abdelmoez³

et al. 2016

IJCSE

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Requirements prioritization plays a significant role to ensure successful planning of software releases. It helps software stakeholders to discover the most desirable requirements. In this paper, we propose requirements prioritization approach using social network analysis based on stakeholders' feedback. The social network models the relationships among stakeholders, their feedback and the system requirements. A string-matching algorithm is used to automate the discovery process of the relationship between stakeholders' feedbacks and requirements specifications based on text similarity. Then, the network is analysed to identify the priority of requirements depending on relevant groups of stakeholders and their feedbacks. As an attempt to validate the results, focus group is conducted to discover the relationship between the stakeholders' feedbacks and the requirements by manually matching each feedback with its related system requirement. Then, social network is built based on the results of the focus group and the system requirements are prioritized based on the network. The results of the two approaches are compared. The experimental results proved that there is a difference between the approach that uses string-matching and the focus group approach. The string-matching approach does not seem to be very appropriate in capturing the real relationship between the stakeholders' feedbacks and the requirements.

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Social Network Analysis: Hardly easy

Cited by 15 publications

References 23 publications

A Survey on Vehicular Social Networks

A Survey on Vehicular Social Networks

Data Transmissions Using Hub Nodes in Vehicular Social Networks

Considering Stakeholders’ Feedback in Requirements Prioritization using Social Network Analysis

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