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Community detection in social networks using user frequent pattern mining

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Abstract

Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including ‘similar’ nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F-measure is used to evaluate the results of two real-world datasets (Blogcatalog and Flickr), demonstrating that the proposed method principals to improve the community detection quality.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Seyed Ahmad Moosavi & Mehrdad Jalali

  2. Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Negin Misaghian

  3. Department of Computer System and Information technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband & Mohammad Hossein Anisi

Authors
  1. Seyed Ahmad Moosavi
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  2. Mehrdad Jalali
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  3. Negin Misaghian
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  4. Shahaboddin Shamshirband
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  5. Mohammad Hossein Anisi
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Corresponding author

Correspondence to Mehrdad Jalali.

Appendix: Structure-based evaluation

Appendix: Structure-based evaluation

We compared our detected communities in terms of structural metrics to other approaches [26, 27].

To demonstrate that the proposed method extracts the consistent community in term of structure and density, we have implemented the proposed method and also two approaches [26, 27] on Last.Fm data set. For approaches in [26, 27], we calculated core communities, each time with different centrality measures (are shown in method column) While in two papers [26, 27], only betweenness, closeness, and degree metrics were mentioned, communities are formed around the cores in each method and are determined by voting from its neighbors (best results are shown from two approaches [26, 27]). We compared our approach with them [26, 27] in terms of structure metrics. Results are shown in Table 6 based on density, diameter and distance. As you can see, even the results of group analysis in our approach are not far from the previous works [26, 27] in terms of average of density, average of distance between nodes, and average of maximum distance (diameter). Results indicate that our approach extracts communication along with similar users and somewhat related. Note the overlap is permitted in each method. This makes the average of density decrease, and average of distance and diameter increase. However, it is intended for all methods. It should be said that the upper the density and lower distance and diameter is better but our approach is not far from the previous approaches, and this shows that our approach is acceptable in extracting the consistent community in term of structure and density; however, our aim is discovery of similar people in terms of performance and relationships in online social networks and is limited to a particular structure and its specific applications.

Table 6 Structure-based evaluation
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Moosavi, S.A., Jalali, M., Misaghian, N. et al. Community detection in social networks using user frequent pattern mining. Knowl Inf Syst 51, 159–186 (2017). https://doi.org/10.1007/s10115-016-0970-8

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