  Social network analysis is a popular discipline among the social and
behavioural sciences, in which the relationships between different social
entities are modelled as a network. One of the most popular problems in social
network analysis is finding communities in its network structure. Usually, a
community in a social network is a functional sub-partition of the graph.
However, as the definition of community is somewhat imprecise, many algorithms
have been proposed to solve this task, each of them focusing on different
social characteristics of the actors and the communities. In this work we
propose to use novel combinations of affinity functions, which are designed to
capture different social mechanics in the network interactions. We use them to
extend already existing community detection algorithms in order to combine the
capacity of the affinity functions to model different social interactions than
those exploited by the original algorithms.

Social and Information Networks