  Network representation learning (NRL) methods have received significant
attention over the last years thanks to their success in several graph analysis
problems, including node classification, link prediction, and clustering. Such
methods aim to map each vertex of the network into a low-dimensional space in a
way that the structural information of the network is preserved. Of particular
interest are methods based on random walks; such methods transform the network
into a collection of node sequences, aiming to learn node representations by
predicting the context of each node within the sequence. In this paper, we
introduce TNE, a generic framework to enhance the embeddings of nodes acquired
by means of random walk-based approaches with topic-based information. Similar
to the concept of topical word embeddings in Natural Language Processing, the
proposed model first assigns each node to a latent community with the favor of
various statistical graph models and community detection methods and then
learns the enhanced topic-aware representations. We evaluate our methodology in
two downstream tasks: node classification and link prediction. The experimental
results demonstrate that by incorporating node and community embeddings, we are
able to outperform widely-known baseline NRL models.
