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Diffusion Maps for Textual Network Embedding
Xinyuan Zhang · Yitong Li · Dinghan Shen · Lawrence Carin

Tue Dec 04 07:25 AM -- 07:30 AM (PST) @ Room 220 E
in Tue Track 2 -- Session 1 »

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.

Author Information

Xinyuan Zhang (Duke University)
Yitong Li (Duke University)
Dinghan Shen (Duke University)
Lawrence Carin (Duke University)

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