Research on a large-scale community detection algorithm based on non-weighted graph

Ai, Fangju

doi:10.1007/s10586-017-1326-1

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Fang-Ju [38] proposed a group centric-based clustering method for detecting community structures in social networks. Nodes that are similar are clustered to a same community.…”

Section: ) Group Centric Clustering Categorymentioning

confidence: 99%

Static and Dynamic Community Detection Methods That Optimize a Specific Objective Function: A Survey and Experimental Evaluation

Taha

2020

IEEE Access

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Most current survey papers classify community detection methods into broad categories and do not draw clear boundaries between the specific techniques employed by these methods. We survey in this paper all fine-grained community detection categories, the clustering methods that fall under these categories, and the techniques employed by these methods for optimizing each objective function. We provide methodology-based taxonomies that classify static and dynamic community detection methods into hierarchically nested, fine-grained, and specific classes. We classify the methods into the objective function they optimize. Each objective function class is classified into clustering categories. Each category is further classified into clustering methods. Methods are further classified into sub-methods and so on. Thus, the lowest subclass in a hierarchy is a fine-grained and specific method. For each method, we survey the different techniques in literature employed by the method. We empirically and experimentally compare and rank the different methods that fall under each clustering category. We also empirically and experimentally compare and rank the different categories that optimize a same objective function. In summary, the blockbased, top-down divisive-based, random walk-based, and matrix eigenvector-based methods achieved good results. Finally, we provide fitness metrics for each objective function.

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“…Fang-Ju [38] proposed a group centric-based clustering method for detecting community structures in social networks. Nodes that are similar are clustered to a same community.…”

Section: ) Group Centric Clustering Categorymentioning

confidence: 99%

Static and Dynamic Community Detection Methods That Optimize a Specific Objective Function: A Survey and Experimental Evaluation

Taha

2020

IEEE Access

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Community detection in social networks using machine learning: a systematic mapping study

Nooribakhsh,

Fernández-Diego,

González-Ladrón-De-Guevara

et al. 2024

Knowl Inf Syst

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One of the important issues in social networks is the social communities which are formed by interactions between its members. Three types of community including overlapping, non-overlapping, and hidden are detected by different approaches. Regarding the importance of community detection in social networks, this paper provides a systematic mapping of machine learning-based community detection approaches. The study aimed to show the type of communities in social networks along with the algorithms of machine learning that have been used for community detection. After carrying out the steps of mapping and removing useless references, 246 papers were selected to answer the questions of this research. The results of the research indicated that unsupervised machine learning-based algorithms with 41.46% (such as k means) are the most used categories to detect communities in social networks due to their low processing overheads. On the other hand, there has been a significant increase in the use of deep learning since 2020 which has sufficient performance for community detection in large-volume data. With regard to the ability of NMI to measure the correlation or similarity between communities, with 53.25%, it is the most frequently used metric to evaluate the performance of community identifications. Furthermore, considering availability, low in size, and lack of multiple edge and loops, dataset Zachary’s Karate Club with 26.42% is the most used dataset for community detection research in social networks.

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Research on a large-scale community detection algorithm based on non-weighted graph

Cited by 2 publications

References 12 publications

Static and Dynamic Community Detection Methods That Optimize a Specific Objective Function: A Survey and Experimental Evaluation

Static and Dynamic Community Detection Methods That Optimize a Specific Objective Function: A Survey and Experimental Evaluation

Community detection in social networks using machine learning: a systematic mapping study

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