Abstract
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.)
This work is supported by the National Science Foundation of China (61472183).
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Notes
- 1.
In fact, the parameters \(p_i\), \(q_i\) can be different for strategies \(\pi _i(x, u_i)\) with different distances \(d(u_{i-1}, x)\). But in this work, we share the parameters across strategy \(\pi _i\) in order to reduce the burden of parameter searching.
- 2.
In the supplementary material (at https://github.com/SongY123/MSNE), we discuss why MSNE with higher order sometimes does not get better results.
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- 4.
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Wang, R., Song, Y., Dai, Xy. (2019). MSNE: A Novel Markov Chain Sampling Strategy for Network Embedding. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_9
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