A novel granular approach for detecting dynamic online communities in social network

Cheraghchi, Hamideh Sadat; Zakerolhosseini, Ali; Bagheri Shouraki,, Saeed and Homayounvala, Elaheh. 2019. A novel granular approach for detecting dynamic online communities in social network. Soft Computing, 23(20), pp. 10339-10360. ISSN 1432-7643 [Article]

SOCO-D-17-00523_R1_3.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

Download (1MB) | Preview

Abstract or Description

The great surge in the research of community discovery in complex network is going on due to its challenging aspects. Dynamicity and overlapping nature are among the common characteristics of these networks which are the main focus of this paper. In this research, we attempt to approximate the granular human-inspired viewpoints of the networks. This is especially helpful when making decisions with partial knowledge. In line with the principle of granular computing, in which precision is avoided, we define the micro- and macrogranules in two levels of nodes and communities, respectively. The proposed algorithm takes microgranules as input and outputs meaningful communities in rough macrocommunity form. For this purpose, the microgranules are drawn toward each other based on a new rough similarity measure defined in this paper. As a result, the structure of communities is revealed and adapted over time, according to the interactions observed in the network, and the number of communities is extracted automatically. The proposed model can deal with both the low and the sharp changes in the network. The algorithm is evaluated in multiple dynamic datasets and the results confirm the superiority of the proposed algorithm in various measures and scenarios.

Item Type:


Identification Number (DOI):


Additional Information:

Social networks have been rapidly growing in recent years. There are many community detection algorithms in use today, however, most of these algorithms are designed to discover communities in static networks and do not scale well, while networks today are continually changing their structure. This work is significant because it proposes a novel way to detect online dynamic communities in social networks, capable of detecting both low and abrupt changes in the network. Detection of online communities provides a valuable insight in many application domains including crime detection, disease spread and many more.


Social network analysis, Dynamic community detection, Granular clustering, Evolutionary clustering

Departments, Centres and Research Units:



30 October 2018Published Online
October 2019Published
29 August 2018Accepted

Item ID:


Date Deposited:

08 Oct 2019 10:48

Last Modified:

09 Jun 2021 13:53

Peer Reviewed:

Yes, this version has been peer-reviewed.



View statistics for this item...

Edit Record Edit Record (login required)