Graph Clustering with Dynamic Embedding

Understand

Graph clustering (or community detection) has long drawn enormous attention from the research on web mining and information networks.

• Recent literature on this topic has reached a consensus that node contents and link structures should be integrated for reliable graph clustering, especially in an unsupervised setting.
• However, existing methods based on shallow models often suffer from content noise and sparsity.
• In this work, we propose to utilize deep embedding for graph clustering, motivated by the well-recognized power of neural networks in learning intrinsic content representations.