历些毛子代枚大学 (2)深度整合网络：DAN XIDIAN UNIVERSITY 主要思路 抽样：random walk+reverse random walk 整合：深度整合，每层单独训练(layer-wise greedy optimization strategy)，堆叠成深度网络 Algorithm 1.Neighborhood Function N(vi) Input:node vi.The transition matrix P and R Output:the sampled neighbors of node vi. 1:P-s-sample p neighbors according to distribution P 2:R-s-sample r neighbors according to distribution Ri. 3:samples P_s,R_s. 4:return samples.（2） 深度整合网络：DAN 15 1 主要思路 • 抽样：random walk + reverse random walk • 整合：深度整合，每层单独训练（ layer-wise greedy optimization strategy ），堆叠成深度网络