A Graph Based Clustering Scheme for Identifying Related Tags in Folksonomies

This paper focuses on social network analysis (SNA) for the purposes of community detection using a hybrid graph-based tag clustering scheme (HGC). The authors present a novel scheme for graph-based clustering with the goal of identifying groups of related tags in folksonomies. Their scheme searches for core sets, or groups of nodes that are densely connected to each other, by efficiently exploring the two-dimensional core parameter space, and successively expands the identified cores by maximizing a local subgraph quality measure. The paper then evaluates this scheme on three real-world tag networks by assessing the relatedness of same-cluster tags and by using tag clusters for tag recommendation. The results are also compared to the ones derived from a baseline graph-based clustering method and from a popular modularity maximization clustering method.

This article will be of particular use to researchers and practitioners interested in exploring methods for community detection using graph-based clustering in social media and social network analysis (SNA) applications. In particular, the article is helpful for those who want to explore social or collaborative tagging on social media platforms and content sharing web applications.