Name
Abstract | ||
|---|---|---|
Network embedding represents nodes in a continuous vector space and preserves structure information from a network. Existing methods usually adopt a “one-size-fits-all” approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding (AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: (1) an attention-based autoencoder that effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training, and (2) an adversarial regularization guides the autoencoder in learning robust representations by matching the posterior distribution of the latent embeddings to a given prior distribution. Experimental results on real-world networks show that the proposed approach outperforms strong baselines. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Vector space,Embedding,Autoencoder,Computer science,Posterior probability,Regularization (mathematics),Artificial intelligence,Network embedding,Prior probability,Machine learning,Adversarial system |
DocType | Volume | Citations |
Journal | abs/1803.09080 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Sang | 1 | 12 | 2.25 |
| Min Xu | 2 | 3 | 3.44 |
| Shengsheng Qian | 3 | 130 | 19.10 |
| Xindong Wu | 4 | 8830 | 503.63 |