Meta-Learning on Graph with Curvature-Based Analysis (Student Abstract)

AAAI 2020 pp. 13875-13876

doi:10.1609/AAAI.V34I10.7210 /aaai/2020/moon2020aaai-meta/

Abstract

Learning latent representations in graphs is finding a mapping that embeds nodes or edges as data points in a low-dimensional vector space. This paper introduces a flexible framework to enhance existing methodologies that have difficulty capturing local proximity and global relationships at the same time. Our approach generates a virtual edge between non-adjacent nodes based on the Forman-Ricci curvature in network. By analyzing the network using topological information, global relationships structurally similar can easily be detected and successfully integrated with previous works.

PDF AAAI Semantic Scholar

Cite

Text

Moon and Lim. "Meta-Learning on Graph with Curvature-Based Analysis (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7210

Markdown

[Moon and Lim. "Meta-Learning on Graph with Curvature-Based Analysis (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/moon2020aaai-meta/) doi:10.1609/AAAI.V34I10.7210

BibTeX

@inproceedings{moon2020aaai-meta,
  title     = {{Meta-Learning on Graph with Curvature-Based Analysis (Student Abstract)}},
  author    = {Moon, Tae Hong and Lim, Sungsu},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13875-13876},
  doi       = {10.1609/AAAI.V34I10.7210},
  url       = {https://mlanthology.org/aaai/2020/moon2020aaai-meta/}
}