Research Article
Multi-order Proximity Graph Structure Embedding
@INPROCEEDINGS{10.1007/978-3-030-92638-0_25, author={Wang Zhang and Lei Jiang and Huailiang Peng and Qiong Dai and Xu Bai}, title={Multi-order Proximity Graph Structure Embedding}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 17th EAI International Conference, CollaborateCom 2021, Virtual Event, October 16-18, 2021, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2022}, month={1}, keywords={Deep learning Unsupervised learning Graph embedding}, doi={10.1007/978-3-030-92638-0_25} }- Wang Zhang
Lei Jiang
Huailiang Peng
Qiong Dai
Xu Bai
Year: 2022
Multi-order Proximity Graph Structure Embedding
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-92638-0_25
Abstract
Graph embedding methods convert the flexible graph structure into low-dimensional representations while maintaining the graph structure information. Most existing methods focus on learning low- or high-order graph information, and cause loss of information during the embedding process. We instead propose a new method that can learn low and high order graph information simultaneously. The method fuses structure-preserving model with random walk sampling, which learns multi-order graph structure information more efficiently. Our method also utilizes distance-based weighted negative samples to improve the representations learning. The experimental results indicate that our proposed method provides very competitive results on the node classification, node clustering and graph reconstruction tasks for four benchmark datasets, BlogCatalog, PPI, Wikipedia and email-Eu-core.
