MSNE: A Novel Markov Chain Sampling Strategy for Network Embedding

Network embedding methods have obtained great progresses on many tasks, such as node classification and link prediction. Sampling strategy is very important in network embedding. It is still a challenge for sampling in a network with complicated topology structure. In this paper, we propose a high-order Markov chain Sampling strategy for Network Embedding (MSNE). MSNE selects the next sampled node based on a distance metric between nodes. Due to high-order sampling, it can exploit the whole sampled path to capture network properties and generate expressive node sequences which are beneficial for downstream tasks. We conduct the experiments on several benchmark datasets. The results show that our model can achieve substantial improvements in two tasks of node classification and link prediction. (Datasets and code are available at https://github.com/SongY123/MSNE.)