Motivating Content Sharing and Trustworthiness in Mobile Social Networks

Awuor, Fredrick Mzee; Wang, Chih-Yu; Tsai, Tzu-Chieh

doi:10.1109/access.2018.2834359

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…Redes sociais móveis ou oportunísticas têm sido amplamente estudadas na literatura [Wang et al 2016, Zhao and Song 2017, Awuor et al 2018, Wu et al 2019. As motivações vão desde o potencial de adoção das mesmas para computação [Trifunovic et al 2017] e caching [Wu et al 2019] oportunísticos, bem como permitir reuso de recursos de redes celulares visando maior eficiência no uso do espectro [Wang et al 2016].…”

Section: Trabalhos Relacionadosunclassified

“…Mais recentemente, Viard et al [Viard et al 2015] identificaram novos padrões de contatos entre pessoas em diferentes escalas temporais, em um experimento em uma escola de ensino médio. Em comum a essas pesquisas, observa-se não apenas o interesse em entender como as interações sociais ocorrem (e mesmo como elas são influenciadas pela tecnologia à disposição [Trifunovic et al 2017]), mas também como a tecnologia pode apoiar essas interações [Awuor et al 2018].…”

Section: Trabalhos Relacionadosunclassified

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Caracterização de Mobilidade e Detecção de Comunidades baseadas em Tópicos de Interesse

Ribeiro¹,

Castanheira²,

Schaeffer-Filho³

et al. 2020

Anais XXXVIII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2020)

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Redes móveis ad hoc impõem desafios como estabelecimento de conexões, descoberta de rotas e duração da rede devido à mobilidade dos nodos. Nesse sentido, pesquisadores vem analisando aspectos de mobilidade, p.ex., tempo de contato e entre-contato dos dispositivos e as comunidades formadas, em cenários como campus universitário, veículos e em conferências. Este trabalho caracteriza a mobilidade de participantes de uma conferência e propõe a detecção de comunidades baseadas em tópicos de interesse. Os dados usados nesse estudo, coletados durante o SBRC 2019 por meio de um app de gamificação, foram anonimizados e modelados como um grafo temporal. Os resultados mostram que, embora cada participante tenha um alto número de contatos, o tempo de contato é baixo. Além disso, as comunidades detectadas a partir dos tópicos de interesse são similares aos k-cliques do grafo temporal.

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Section: Trabalhos Relacionadosunclassified

Caracterização de Mobilidade e Detecção de Comunidades baseadas em Tópicos de Interesse

Ribeiro¹,

Castanheira²,

Schaeffer-Filho³

et al. 2020

Anais XXXVIII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2020)

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“…Therefore, it is difficult to select the information that users want and to find users who provide the information they want among many users [13][14][15][16][17]. In addition, it is not guaranteed that a user connected to a network relationship is an expert who can provide a high quality response for user questions [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Expert Finding Considering Dynamic Profiles and Trust in Social Networks

et al. 2019

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Recently, social network services that express individual opinions and thoughts have been significantly developed. As unreliable information is generated and shared by arbitrary users in social network services, many studies have been conducted to find users who provide reliable and professional information. In this paper, we propose an expert finding scheme to discover users who can answer users’ questions professionally in social network services. We use a dynamic profile to extract the user’s latest interest through an analysis of the user’s recent activity. To improve the accuracy of the expert finding results, we consider the user trust and response quality. We conduct a performance evaluation with the existing schemes through various experiments to verify the superiority of the proposed scheme.

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“…In recent years, researchers in many fields have analyzed the social relationships and mobility patterns of carriers. [3][4][5] In mobile social networks, the links between nodes change dynamically, and the nodes in the network are sparse.…”

Section: Introductionmentioning

confidence: 99%

“…With the extensive use of various personal mobile devices, mobile social networks have gained rapid development. 5,7 In mobile social networks, people carry wireless mobile devices to participate in various activities.…”

Section: Introductionmentioning

confidence: 99%

Efficient opportunistic routing with social context awareness for distributed mobile social networks

Xiao

Deng

et al. 2019

Concurrency and Computation

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Mobile social networks (MSNs) are developed from mobile ad hoc networks. Nodes in such networks usually have social characteristics. In recent years, researchers are trying to use the social characteristics of the network to propose new data forwarding metrics, so as to design more efficient routing algorithms. However, most of the proposed algorithms only consider local context information, which leads to the performance of the routing is not optimized enough.In this paper, we introduce two key metrics, namely, social relationship and social activity.The metrics will be used to search the best data forwarding nodes to improve the probability of data delivery. We propose a prediction-based social-aware opportunistic routing (PSOR). In the proposed method, node's social profiles are used to search relay candidates set, and the discrete-time semi-Markov prediction model is used to find the probability distribution of node transition between communities. Many simulation experiments based on real traces show that the proposed PSOR algorithm is more efficient to maximize the packet delivery probability than other state-of-the-art algorithms.

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Motivating Content Sharing and Trustworthiness in Mobile Social Networks

Cited by 11 publications

References 32 publications

Caracterização de Mobilidade e Detecção de Comunidades baseadas em Tópicos de Interesse

Caracterização de Mobilidade e Detecção de Comunidades baseadas em Tópicos de Interesse

Expert Finding Considering Dynamic Profiles and Trust in Social Networks

Efficient opportunistic routing with social context awareness for distributed mobile social networks

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