Abstract
In social tagging systems, also known as folksonomies, users collaboratively
manage tags to annotate resources. Naturally, social tagging systems can be
modeled as a tripartite hypergraph, where there are three different types of
nodes, namely users, resources and tags, and each hyperedge has three end
nodes, connecting a user, a resource and a tag that the user employs to
annotate the resource. Then, how can we automatically detect user, resource and
tag communities from the tripartite hypergraph? In this paper, by turning the
problem into a problem of finding an efficient compression of the hypergraph's
structure, we propose a quality function for measuring the goodness of
partitions of a tripartite hypergraph into communities. Later, we develop a
fast community detection algorithm based on minimizing the quality function. We
explain advantages of our method and validate it by comparing with various
state of the art techniques in a set of synthetic datasets.
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