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)        
        
   