Core-Based Dynamic Community Detection in Mobile  Social Networks

Xu, Hao; Hu, Yanli; Wang, Zhenwen; Ma, Jianwei; Xiao, Weidong

doi:10.3390/e15125419

Cited by 17 publications

(6 citation statements)

References 29 publications

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“…Besides, there are also researchers to investigate the dynamics of the community (community evolution) to study the dynamics of complex networks [43]. Such methods include core-based community evolution mechanism (CoCE) [44], FacetNet [45], GraphScope [46] and group evolution discovery (GED) method [47].…”

Section: B Topic Changes In Social Media Messages During Disastersmentioning

confidence: 99%

Community Evolutional Network for Situation Awareness Using Social Media

Wang

et al. 2020

IEEE Access

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Social media is important for situational awareness during a disaster. During a disaster, the situation of emergence often changes over time and hence the topics of social media messages generated by social media users also change accordingly. Few studies quantitatively describe the topic evolution of social media during a disaster and the corresponding relationship between topic evolution and disaster process. We address this problem using co-word network analysis and present a new method based on the community evolution of the co-word network to analyze topic evolution over time in social media. The method uses communities of the co-word network in social media to represent topics. Based on the theory of community evolution, a community evolutional network is proposed to support and quantify the evolution of the topics. We implemented the proposed method in a case study, ''July 2012 Beijing flood'' using the Sina Weibo dataset. Results show that our method can well quantify the evolution process of topics and validate the effectiveness of our method in real-world applications. The method can facilitate the understanding of public expression dynamics during a disaster and be used to reveal the process and stages of a disaster.

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Section: B Topic Changes In Social Media Messages During Disastersmentioning

confidence: 99%

Community Evolutional Network for Situation Awareness Using Social Media

Wang

et al. 2020

IEEE Access

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“…Esta seção discute como as comunidades são formadas por tópicos de interesse. Na literatura, existem algoritmos conhecidos para identificação de comunidades [Palla et al 2005, Nguyen et al 2011, Xu et al 2013] que podem apresentar desempenhos distintos dependendo das características do grafo que serão utilizadas para identificação de comunidades. A partir da estrutura de dados criada com as informações do evento, entretanto, é possível a identificação das comunidades considerando o interesse do congressista sem a necessidade de se utilizar esses algoritmos.…”

Section: Detecção De Comunidadesunclassified

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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“…Due to the huge amount of data generated from social networks, many researchers investigated several research problems, such as predicting image popularity [16][17][18][19][20][21][22], identifying influential users [31,32], characterizing user behavior [33][34][35][36][37], and detecting community evolution [38,39]. In this paper, the problem of image popularity is addressed.…”

Section: Social Network Analysismentioning

confidence: 99%

Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis

Almgren

Kim²,

Lee

2017

Entropy

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Topological data analysis is a noble approach to extract meaningful information from high-dimensional data and is robust to noise. It is based on topology, which aims to study the geometric shape of data. In order to apply topological data analysis, an algorithm called mapper is adopted. The output from mapper is a simplicial complex that represents a set of connected clusters of data points. In this paper, we explore the feasibility of topological data analysis for mining social network data by addressing the problem of image popularity. We randomly crawl images from Instagram and analyze the effects of social context and image content on an image's popularity using mapper. Mapper clusters the images using each feature, and the ratio of popularity in each cluster is computed to determine the clusters with a high or low possibility of popularity. Then, the popularity of images are predicted to evaluate the accuracy of topological data analysis. This approach is further compared with traditional clustering algorithms, including k-means and hierarchical clustering, in terms of accuracy, and the results show that topological data analysis outperforms the others. Moreover, topological data analysis provides meaningful information based on the connectivity between the clusters.

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Core-Based Dynamic Community Detection in Mobile Social Networks

Cited by 17 publications

References 29 publications

Community Evolutional Network for Situation Awareness Using Social Media

Community Evolutional Network for Situation Awareness Using Social Media

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

Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis

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