  In this paper we introduce the concept of network semantic segmentation for
social network analysis. We consider the GitHub social coding network which has
been a center of attention for both researchers and software developers.
Network semantic segmentation describes the process of associating each user
with a class label such as a topic of interest. We augment node attributes with
network significant connections and then employ machine learning approaches to
cluster the users. We compare the results with a network segmentation performed
using community detection algorithms and one executed by clustering with node
attributes. Results are compared in terms of community diversity within the
semantic segments along with topic
