Reviews: Diffusion Maps for Textual Network Embedding

The main idea of this paper is to use the diffusion convolutional operator to learn text embedding that takes into account the global influence of the whole graph. It then incorporates the diffusion process in the loss function to capture high-order proximity. In contrast, previous works either neglect the semantic distance indicated from the graph, or fails to take into account the similarities of context influenced by global structural information. The author then conducts experiments on the task of multi-label classification of text and link prediction and shows that the proposed model outperforms the baselines. Strength: The high level idea of of this paper is good, and the method is novel.