Fast Sequence-Based Embedding with Diffusion Graphs
This work addresses the need for faster and more accurate graph embeddings for tasks like community detection, though it appears incremental as it builds on existing sequence-based methods.
The paper tackled the problem of generating vertex sequences for network embedding by proposing diffusion graphs, which improved computational efficiency and accuracy, particularly showing better performance in community detection tasks as edge density increased.
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based embedding procedures use features extracted from linear sequences of nodes to create embeddings using a neural network. In this paper, we propose diffusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more accurate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph. In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence-based embedding methods.