  Textual network embedding leverages rich text information associated with the
network to learn low-dimensional vectorial representations of vertices. Rather
than using typical natural language processing (NLP) approaches, recent
research exploits the relationship of texts on the same edge to graphically
embed text. However, these models neglect to measure the complete level of
connectivity between any two texts in the graph. We present diffusion maps for
textual network embedding (DMTE), integrating global structural information of
the graph to capture the semantic relatedness between texts, with a
diffusion-convolution operation applied on the text inputs. In addition, a new
objective function is designed to efficiently preserve the high-order proximity
using the graph diffusion. Experimental results show that the proposed approach
outperforms state-of-the-art methods on the vertex-classification and
link-prediction tasks.
