دوشنبه 17 اردیبهشت 1403
| عنوان مقاله | |
| عنوان مقاله |
An Efficient Modularity based Algorithm for Community Detection in Social Network |
| عنوان فارسی مقاله | یک الگوریتم کارآمد مبتنی بر مدولار برای شناسایی جامعه در شبکه های اجتماعی |
| مشخصات مقاله انگلیسی | |
| نشریه: Ieee |
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| سال انتشار |
2016 |
| عنوان مجله |
2016 International Conference on Internet of Things and Applications |
| تعداد صفحات مقاله انگلیسی | 6 |
| رفرنس | دارد |
| تعداد رفرنس | 35 |
| چکیده مقاله | |
| چکیده |
Community detection process intends to detect clusters in a social network (SN), where nodes within the cluster are densely connected as compared to nodes outside the cluster. This process is one of the challenging issues in era of big data analytics particularly in the area of social networking. Graph data structure is often used to represent SN, where nodes can be used to represent actors and edges can be used to represent relationships among the actors. There are several algorithms for community detection purpose in a SN but each one has certain drawbacks in detecting community over a large scale network. In this paper an efficient modularity based community detection algorithm has been proposed. The proposed algorithm has been compared with other existing community detection algorithms using some of the most popular social network datasets. Performance of the algorithm has been assessed using various parameters like modularity, clustering coefficient, execution time etc. |
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| دانلود | |
