Online Factorization and Partition of Complex Networks From Random Walks

Understand

Finding the reduced-dimensional structure is critical to understanding complex networks.

• Existing approaches such as spectral clustering are applicable only when the full network is explicitly observed.
• In this paper, we focus on the online factorization and partition of implicit large-scale networks based on observations from an associated random walk.
• We formulate this into a nonconvex stochastic factorization problem and propose an efficient and scalable stochastic generalized Hebbian algorithm.