Large-scale Graph Embedding
Project Description:
Study the way to embed graphs, i.e., generating a low-dimensional vector representation for each vertex by capturing the graph structure.
Project Duration:
11/2016 to 05/2017
Project Significance:
Graph embedding can be used on many applications, e.g, clustering, classification, link prediction, etc.
Results Achieved:
1 conf. paper (CIKM 2017)