  Seeding then expanding is a commonly used scheme to discover overlapping
communities in a network. Most seeding methods are either too complex to scale
to large networks or too simple to select high-quality seeds, and the
non-principled functions used by most expanding methods lead to poor
performance when applied to diverse networks. This paper proposes a new method
that transforms a network into a corpus where each edge is treated as a
document, and all nodes of the network are treated as terms of the corpus. An
effective seeding method is also proposed that selects seeds as a training set,
then a principled expanding method based on semi-supervised learning is applied
to classify edges. We compare our new algorithm with four other community
detection algorithms on a wide range of synthetic and empirical networks.
Experimental results show that the new algorithm can significantly improve
clustering performance in most cases. Furthermore, the time complexity of the
new algorithm is linear to the number of edges, and this low complexity makes
the new algorithm scalable to large networks.
