  Network embeddings have become very popular in learning effective feature
representations of networks. Motivated by the recent successes of embeddings in
natural language processing, researchers have tried to find network embeddings
in order to exploit machine learning algorithms for mining tasks like node
classification and edge prediction. However, most of the work focuses on
finding distributed representations of nodes, which are inherently ill-suited
to tasks such as community detection which are intuitively dependent on
subgraphs.
  Here, we propose sub2vec, an unsupervised scalable algorithm to learn feature
representations of arbitrary subgraphs. We provide means to characterize
similarties between subgraphs and provide theoretical analysis of sub2vec and
demonstrate that it preserves the so-called local proximity. We also highlight
the usability of sub2vec by leveraging it for network mining tasks, like
community detection. We show that sub2vec gets significant gains over
state-of-the-art methods and node-embedding methods. In particular, sub2vec
offers an approach to generate a richer vocabulary of features of subgraphs to
support representation and reasoning.
