An efficient method to detect communities in social networks using DBSCAN algorithm

The detection of the communities and the depiction of the interactions, between the entities and the individuals in the real world network graphs is a challenging problem. There are many conventional ways to detect those interconnected nodes which lead to the detection of communities. The strength of the detected communities can be detected by its modularity which is a measurement of the structure of a graph, and increasing the modularity is also a bit challenging problem. So, in this work, the DBSCAN clustering algorithm has been implemented for the task of detecting the outliers in the process of detecting the communities in a social network, and those outliers which are also known as “noisy nodes”, are removed from the main formed network graph. The proposed algorithm in this paper, mainly focuses on the detection and removal of those noisy nodes or outliers in the detected communities which leads to the improvement of the quality of the detected communities. In previous community detection algorithms, some algorithms needed the number of communities prior to the formation of communities which precludes from forming a good community, while some algorithms cannot operate with the huge amount of data and some algorithms require a huge amount of memory. The proposed algorithm does not require any prior mentioning of the number of communities, it has also been tested with large networks with a size of more than 1000 nodes and it does not require much space. Therefore, the proposed algorithm has overcome the mentioned limitations of the previous community detection algorithms. The data have been collected from the social network websites-Facebook and Twitter. The communities formed from the proposed algorithm have been compared with the results of the four other community detection algorithms, i.e., with the Louvain algorithm, Walktrap algorithm, Leading eigenvector algorithm, and Fastgreedy algorithm. The proposed methodology performs well for the detection of communities with the increment of the strength of the detected communities.