Community detection in node-attributed social networks: A survey

Chunaev, Petr

doi:10.1016/j.cosrev.2020.100286

Cited by 190 publications

(80 citation statements)

References 110 publications

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“…e method was only applicable to the network with assortative structures and failed to detect generalized community. More discussions on attribute networks can be found in related surveys by Bothorel et al [34] and Chunaev [35].…”

Section: Related Workmentioning

confidence: 99%

A Unified Bayesian Model for Generalized Community Detection in Attribute Networks

et al. 2020

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Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer links between different communities, which ignore the rich diversity of community regularities in real networks. In addition, the node attributes that provide rich semantics information of communities and networks can facilitate in-depth community detection of structural information. In this paper, we propose a novel unified Bayesian generative model to detect generalized communities and provide semantic descriptions simultaneously by combining network topology and node attributes. The proposed model is composed of two closely correlated parts by a transition matrix; we first apply the concept of a mixture model to describe network regularities and then adjust the classic Latent Dirichlet Allocation (LDA) topic model to identify community semantically. Thus, the model can detect broad types of network structure regularities, including assortative structures, disassortative structures, and mixture structures and provide multiple semantic descriptions for the communities. To optimize the objective function of the model, we use an effective Gibbs sampling algorithm. Experiments on a number of synthetic and real networks show that our model has superior performance compared with some baselines on community detection.

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Section: Related Workmentioning

confidence: 99%

A Unified Bayesian Model for Generalized Community Detection in Attribute Networks

et al. 2020

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“…This method does not recommend large graphs due to high execution time. 2,30 Some community search methods are metric-based. They are classified according to different criteria as k-core, location, k-truss, k-clique, K-ECC, etc.…”

Section: Communities Detection By Futuristic Approachmentioning

confidence: 99%

“…In this method, the similarities of features are calculated by the Gaussian kernel function. 30,34 Other hybrid approaches detect communities in GEO-Social networks by spatial and social relationships among users. 35 Another hybrid method is ISOFDP.…”

Section: Communities Detection By Futuristic Approachmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

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“…The second main area of multi-view clustering of social-based data are attributed networks. Attributed networks are networks which also have additional information on the nodes (Chunaev 2019). So, an attributed network will have two views of data; one view which is the network itself and a second view of features describing the nodes present in the network.…”

Section: Multi-view Clusteringmentioning

confidence: 99%

“…So, an attributed network will have two views of data; one view which is the network itself and a second view of features describing the nodes present in the network. Attributed networks are often clustered by either combining the attribute information into the network itself and then clustering that network by standard network clustering techniques (Alinezhad et al 2019;Chunaev 2019;Papadopoulos et al 2015;Papadopoulos et al 2017), or by defining a new measure of network modularity that incorporates a term for the attributes (Combe et al 2015;Chunaev 2019). So, attributed network clustering is limited to just the multi-view data scenario in which there is one network view and one non-network view of the data.…”

Section: Multi-view Clusteringmentioning

confidence: 99%

Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering

Cruickshank

Carley

2020

Appl Netw Sci

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The COVID-19 pandemic has produced a flurry of online activity on social media sites. As such, analysis of social media data during the COVID-19 pandemic can produce unique insights into discussion topics and how those topics evolve over the course of the pandemic. In this study, we propose analyzing discussion topics on Twitter by clustering hashtags. In order to obtain high-quality clusters of the Twitter hashtags, we also propose a novel multi-view clustering technique that incorporates multiple different data types that can be used to describe how users interact with hashtags. The results of our multi-view clustering show that there are distinct temporal and topical trends present within COVID-19 twitter discussion. In particular, we find that some topical clusters of hashtags shift over the course of the pandemic, while others are persistent throughout, and that there are distinct temporal trends in hashtag usage. This study is the first to use multi-view clustering to analyze hashtags and the first analysis of the greater trends of discussion occurring online during the COVID-19 pandemic.

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Community detection in node-attributed social networks: A survey

Cited by 190 publications

References 110 publications

A Unified Bayesian Model for Generalized Community Detection in Attribute Networks

A Unified Bayesian Model for Generalized Community Detection in Attribute Networks

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering

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