Different Approaches to Community Detection

Rosvall, Martin; Delvenne, Jean‐Charles; Lambiotte, Renaud

doi:10.1002/9781119483298.ch4

Cited by 36 publications

(40 citation statements)

References 84 publications

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“…Recently, several researches have addressed community detection in heterogeneous multirelational networks. We can divide the related work into five categories based on the techniques used on the multidimensional community detection process, which are modularity optimization, 27 tensor factorization, 28 matrix factorization, 29 stochastic models based approach, 30 and label propagation based approach 31 …”

Section: Related Workmentioning

confidence: 99%

Multidimensional community discovering in heterogeneous social networks

Guesmi

Trabelsi

Latiri

2020

Concurrency and Computation

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Summary Multidimensional community discovering in heterogeneous social networks is an important issue. Many approaches have been proposed for community discovering in heterogeneous networks. However, they have focused only on topological properties of these networks, ignoring the embedded semantic information. As the solution to this information glut limit, we propose, in this article, a new multidimensional community discovering approach, which incorporates the multiple types of objects and relationships, derived from a heterogeneous networks. First, we propose to construct the concept lattice family CLF to represent the different objects and relations of the heterogeneous social networks based on the relational concept analysis techniques. Then after we introduce a new algorithm that explores such CLF and extract the multidimensional communities. Carried out experiments on real datasets enhance the effectiveness of our proposal and open promising issues.

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Section: Related Workmentioning

confidence: 99%

Multidimensional community discovering in heterogeneous social networks

Guesmi

Trabelsi

Latiri

2020

Concurrency and Computation

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“…More modern methods based on embedding communities in low-dimensional vector spaces try to solve problems such as node clustering, node classification, low-dimensional visualizations, edges prediction, among others with great success [33,34]. However, we shall point out that this is a very active area of research with many facets, and as argued in [35], community detection should not be considered as a well-defined problem, but instead, should be motivated by particular reasons. In this sense, our motivation for detecting communities is to find groups of people with a clear topic of interest, regardless of whether such groups of people follow each other on Twitter.…”

Section: Related Workmentioning

confidence: 99%

Characterization of citizens using word2vec and latent topic analysis in a large set of tweets

Vargas-Calderón

Camargo

2019

Cities

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With the increasing use of the Internet and mobile devices, social networks are becoming the most used media to communicate citizens' ideas and thoughts.This information is very useful to identify communities with common ideas based on what they publish in the network. This paper presents a method to automatically detect city communities based on machine learning techniques applied to a set of tweets from Bogotá's citizens. An analysis was performed in a collection of 2,634,176 tweets gathered from Twitter in a period of six months.Results show that the proposed method is an interesting tool to characterize a city population based on a machine learning methods and text analytics. reducing the digital gap in Colombia. For instance, from 2011 to 2014, several portions of the city have experienced an increase of up to 148.3% in homes with Internet connection [2]. By 2011, both Facebook and Twitter were the most popular social networks in Bogotá [1]. Moreover, 65.5% of Bogotá's Internet users used social networks by 2014 [2]. This study concentrates on Twitter because it allows its users to write posts with a length ranging from 1 to 140 characters 1 , making Twitter a tool for microblogging, a form of communication in which users express their opinion about several topics in short posts (tweets) [3].By only taking the users' posts and not the explicit relations between the users, our large data set is an interesting basis for testing unsupervised community detection methodologies. In fact, the only common factor that relates the users from our data set is that they are connected by geographical location.Such unsupervised community detection methodologies help to understand social phenomena that takes place in that geographical region and in a particular period of using data from any social network with no a priori knowledge of people's relations.With the objective of identifying the main topics treated by Bogotá's population on Twitter, as well as detecting possible communities, we collected tweets emitted from Bogotá. Then, a Word2Vec model [4] was built in order to represent the set of tweets corresponding to each user as a vector in a vector space.The gap statistic [5] was used to estimate the number of clusters that could be formed using these vectors. Finally, a frequency distribution of words was built for each cluster, so that each cluster could be identified by its most frequent words, which ultimately characterizes a topic.Our work acknowledges that online social networks constitute one of the main scenarios where people express their opinions, which makes them an outstanding source of information that allows the characterization of important topics for the citizenship. Therefore, we provide a robust method for studying 1 Data was collected before the change in the maximum tweet length to 280 characters

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“…Therefore, there is no single 'true' network partition (Peel et al, 2017). Instead, the method one applies should match the question one aims to answer (Ghasemian et al, 2019;Rosvall et al, 2018). For example, community detection using the group model (Allesina and Pascual, 2009) can provide insights into the roles species play in the network with groups of species that feed on, or are fed by, similar species -insights that cannot be obtained by identifying flow modules or maximising internal density.…”

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confidence: 99%

Identifying flow modules in ecological networks using Infomap

Farage

Edler

Eklöf

et al. 2020

Preprint

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1. Ecological networks often have a modular structure with groups of nodes that interact strongly within the group and weakly with other nodes in the network. A modular structure can reveal underlying dynamic ecological or evolutionary processes, influence dynamics that operate on the network, and affect network stability. Consequently, detecting modular structures is a fundamental analysis in network ecology.2. Although many ecological networks describe flows, such as biomass flows in food webs, disease transmission or temporal interaction networks, most modularity analyses have ignored these dynamics, which can hinder our understanding of the interplay between structure and dynamics. Here we present Infomap, an established community-detection method that maps flows on networks into modules based on random walks. To illustrate and facilitate the usage of Infomap on ecological networks, we also provide a fully documented repository with technical explanations and examples, and an open-source R package, infomapecology.3. Infomap is a flexible tool that works on many network types, including directed and undirected networks, bipartite (e.g., plant-pollinator) and unipartite (e.g., food webs) networks, and multilayer networks (e.g., spatial, temporal or multiplex networks with several interaction types). Infomap can also use additional relevant ecological data, which we illustrate by investigating how taxonomic affiliation as node metadata influences the modular structure. 4. Flow-based modularity analysis is relevant across all of ecology and transcends to other biological and non-biological disciplines. It includes natural ways to analyse directed networks, take advantage of metadata, and analyse multilayer networks. A dynamical approach to detecting modular structure has strong potential to provide new insights into the organisation of ecological networks, that are more relevant to the system/question at hand, compared to methods that ignore dynamics.

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Different Approaches to Community Detection

Cited by 36 publications

References 84 publications

Multidimensional community discovering in heterogeneous social networks

Multidimensional community discovering in heterogeneous social networks

Characterization of citizens using word2vec and latent topic analysis in a large set of tweets

Identifying flow modules in ecological networks using Infomap

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