Diffusion Maps for Textual Network Embedding
Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin
Abstract
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.
BibTeX
@inproceedings{NEURIPS2018_211a7a84,
author = {Zhang, Xinyuan and Li, Yitong and Shen, Dinghan and Carin, Lawrence},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Diffusion Maps for Textual Network Embedding},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/211a7a84d3d5ce4d80347da11e0c85ed-Paper.pdf},
volume = {31},
year = {2018}
}