  Network representation learning has exploded recently. However, existing
studies usually reconstruct networks as sequences or matrices, which may cause
information bias or sparsity problem during model training. Inspired by a
cognitive model of human memory, we propose a network representation learning
scheme. In this scheme, we learn node embeddings by adjusting the proximity of
nodes traversing the spreading structure of the network. Our proposed method
shows a significant improvement in multiple analysis tasks based on various
real-world networks, ranging from semantic networks to protein interaction
networks, international trade networks, human behavior networks, etc. In
particular, our model can effectively discover the hierarchical structures in
networks. The well-organized model training speeds up the convergence to only a
small number of iterations, and the training time is linear with respect to the
edge numbers.
