Name
Abstract | ||
|---|---|---|
Recent advances in the field of network embedding have shown that low-dimensional network representation is playing a critical role in network analysis. Most existing network embedding methods encode the local proximity of a node, such as the first- and second-order proximities. While being efficient, these methods are short of leveraging the global structural information between nodes distant from each other. In addition, most existing methods learn embeddings on one single fixed network, and thus cannot be generalized to unseen nodes or networks without retraining. In this paper we present SPINE, a method that can jointly capture the local proximity and proximities at any distance, while being inductive to efficiently deal with unseen nodes or networks. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.24963/ijcai.2019/333 | international joint conference on artificial intelligence |
Field | DocType | Citations |
Computer science,Theoretical computer science,Artificial intelligence,Network embedding,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junliang Guo | 1 | 1 | 0.68 |
| Linli Xu | 2 | 790 | 42.51 |
| Jingchang Liu | 3 | 2 | 2.06 |