Abstract With continuous development of internet technology, the scope of network datasets increases massively. Analyzing the structure of network data is a research hotspot in machine learning and network applications. In this paper, a scalable online learning algorithm is proposed to speed up the inference procedure for the latent structure of weighted networks. Firstly, the exponential family distribution is utilized to represent the generative process of weighted networks. Then, using stochastic variational inference technique, the online-weighted stochastic block model (ON-WSBM) is developed to efficiently approximate the posterior distribution of underlying block structure. In ON-WSBM an incremental approach based on the subsampling method is adopted to reduce the time complexity of optimization, and then the stochastic optimization method is employed by using natural gradient to simplify the calculation and further accelerate the learning procedure. Extensive experiments on four popular datasets demonstrate that ON-WSBM can efficiently capture the community structure of the complex weighted networks, and can achieve comparatively high prediction accuracy in a short time.
2016, Vol. 29 
