Comparative Analysis of Community Detection Algorithms on the SNAP Social Circles Dataset
This research provides guidance for applying community detection methods to real-world social networks, but it is incremental as it compares existing algorithms without introducing new ones.
The paper tackled the problem of evaluating community detection algorithms on the SNAP Social Circles Dataset from Facebook, finding insights into their effectiveness in detecting meaningful communities based on metrics like modularity and silhouette score.
In network research, Community Detection has always been a topic of significant interest in network science, with numerous papers and algorithms proposing to uncover the underlying structures within networks. In this paper, we conduct a comparative analysis of several prominent community detection algorithms applied to the SNAP Social Circles Dataset, derived from the Facebook Social Media network. The algorithms implemented include Louvain, Girvan-Newman, Spectral Clustering, K-Means Clustering, etc. We evaluate the performance of these algorithms based on various metrics such as modularity, normalized cut-ratio, silhouette score, compactness, and separability. Our findings reveal insights into the effectiveness of each algorithm in detecting various meaningful communities within the social network, shedding light on their strength and limitations. This research contributes to the understanding of community detection methods and provides valuable guidance for their application in analyzing real-world social networks.