2018/02/12 by Junliang Guo, Guo, Junliang, Linli Xu +3 · 1 citation
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #cs.SI
paper · pdf · doi:10.48550/arxiv.1802.03984
IJCAI 2019
openalex publication_date 2018/02/12 · arxiv created 2019/06/10 · arxiv updated 2019/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.