Survey on Social Community Detection

Plantié, Michel; Crampes, Michel

doi:10.1007/978-1-4471-4555-4_4

Cited by 145 publications

(61 citation statements)

References 66 publications

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“…However, defining a community is quite a challenging task [3]. The most commonly used definition is that of [11]: "network communities are groups of vertices within a network that have a high density of within-group connections but a lower density of between-group connection".…”

Section: Communities and Contexts In Dynamic Social Networkmentioning

confidence: 99%

“…The most commonly used definition is that of [11]: "network communities are groups of vertices within a network that have a high density of within-group connections but a lower density of between-group connection". Despite its widespread use [3], this definition considers only the structural aspect of the community. This is why others community definitions based on vertex similarity are proposed.…”

Section: Communities and Contexts In Dynamic Social Networkmentioning

confidence: 99%

“…In this setting, most widespread community detection approaches consider the social network as a graph and then analyze its structure with graph properties and algorithms built around its structure [3]. Even more, in some very influential works in the literature such as [4], the terms "graph" and "network" are used interchangeably [5].…”

Section: Introductionmentioning

confidence: 99%

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Towards Contextualizing Community Detection in Dynamic Social Networks

Rebhi

Yahia

Saoud

et al. 2017

Modeling and Using Context

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Abstract. With the growing number of users and the huge amount of information in dynamic social networks, contextualizing community detection has been a challenging task. Thus, modeling these social networks is a key issue for the process of contextualized community detection. In this work, we propose a temporal multiplex information graphbased model to represent dynamic social networks: we consider simultaneously the social network dynamicity, its structure (different social connections) and various members' profiles so as to calculate similarities between "nodes" in each specific context. Finally a comparative study on a real social network shows the efficiency of our approach and illustrates practical uses.

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Section: Communities and Contexts In Dynamic Social Networkmentioning

confidence: 99%

Section: Communities and Contexts In Dynamic Social Networkmentioning

confidence: 99%

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Towards Contextualizing Community Detection in Dynamic Social Networks

Rebhi

Yahia

Saoud

et al. 2017

Modeling and Using Context

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“…Un grand nombre de méthodes de détection de communautés ont récemment été publiées, ainsi que plusieurs travaux de classification. Les plus significatifs pour notre domaine sont (Plantié et al, 2013, Fortunato, 2010, Porter et al, 2009. Les classifications s'appuient sur des définitions similaires de la notion de communauté mais avec des points de vue différents.…”

Section: Découverte Des Communautés Webunclassified

“…(Plantié et al, 2013) après avoir présenté les classification existantes, adoptent le point de vue de la structure des graphes et de leurs capacités de modélisation des relations complexes. Ainsi il obtiennent trois grandes catégories d'approches :…”

Section: Découverte Des Communautés Webunclassified

Apports des réseaux sociaux pour la gestion de la relation client

Basaille-Gahitte¹,

Abrouk²,

Cullot³

et al. 2014

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RÉSUMÉ. Depuis quelques années, le Web s'est transformé en une plateforme d'échanges. La gestion de relation client doit évoluer pour tirer partie des données disponibles sur les ré-seaux sociaux et mettre l'entreprise au coeur des échanges. Nous proposons dans cet article une approche générique de détection de communautés de clients d'une entreprise, basée sur leur comportement explicite et implicite, intégrant des données de sources diverses. Nous définis-sons une mesure de similarité, entre un utilisateur et un tag, prenant en compte la notation et la consultation des ressources et le réseau social de l'utilisateur. Nous validons cette approche sur une base exemple en utilisant deux méthodes de détection de communautés pour trois cas d'utilisation.ABSTRACT. In recent years, the Web has evolved into an exchange platform. Customer relationship management must follow this evolution and provide users with tools to integrate data from social networks in order to place companies at the heart of the exchanges. We propose in this paper a generic approach to community detection of customers of a company based on their explicit and implicit behavior, integrating data from various sources. For this, we define a similarity measure, between a user and a tag, that takes into account the rating and consultation of resources and the social network of the user. We validate this approach against a test database by using two different community detection methods for three use cases.

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Geovisualization of Social Media

Croitoru

Crooks

Radzikowski

et al. 2017

International Encyclopedia of Geography

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The proliferation of social media over the last decade is presenting substantial computational challenges associated with the management, processing, analysis, and visualization of the corresponding massive volumes of data. Furthermore, this new form of information also imposes newfound challenges upon the geographical community due to the unique nature of its content, as analyzing such data calls for a hybrid mix of spatial and social analysis. The spatial content of social media comprises primarily coordinates from which the contributions originate, or references to specific locations. At the same time, these data have a strong social component, as they can reveal the underlying social structure of the user community through manifestations of their interactions. Analyzing both the spatial and social content of social media feeds is referred to as geosocial analysis. Within this entry we explore the geovisualization opportunities and challenges that are emerging as social media are becoming the subject of study of the geographical community.

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Survey on Social Community Detection

Cited by 145 publications

References 66 publications

Towards Contextualizing Community Detection in Dynamic Social Networks

Towards Contextualizing Community Detection in Dynamic Social Networks

Apports des réseaux sociaux pour la gestion de la relation client

Geovisualization of Social Media

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