Results 41 to 50 of about 8,038,825 (297)

Representation Learning for Spatial Graphs

CoRR, 2018
4 pages, 1 figure ...
Zheng Wang 0046, Ce Ju, Gao Cong, Cheng Long 0001   +3 more
openaire   +2 more sources

A Graph Rewriting Visual Language for Database Programming [PDF]

, 1997
Textual database programming languages are computationally complete, but have the disadvantage of giving the user a non-intuitive view of the database information that is being manipulated.
Rodgers, Peter, Rodgers, Peter J.H., King, P.J.H., Rodgers, P.J.   +3 more
core   +1 more source

Inductive Representation Learning on Temporal Graphs

CoRR, 2020
Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic graphs requires handling new nodes as well as capturing temporal patterns. The node embeddings, which are now functions of time, should represent both the static node features ...
Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan   +4 more
openaire   +4 more sources

A review on graph-based semi-supervised learning methods for hyperspectral image classification

Egyptian Journal of Remote Sensing and Space Sciences, 2020
In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial ...
Shrutika S. Sawant, Manoharan Prabukumar
doaj   +1 more source

Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection

Applied Sciences, 2023
In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance.
Bader Alwasel, Abdulaziz Aldribi, Mohammed Alreshoodi, Ibrahim S. Alsukayti, Mohammed Alsuhaibani   +4 more
doaj   +1 more source

A Survey on Graph Representation Learning Methods [PDF]

, 2022
Graphs representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately.
Khoshraftar, Shima, An, Aijun
core   +1 more source

PocketGraph : graph representation of binding site volumes [PDF]

, 2009
The representation of small molecules as molecular graphs is a common technique in various fields of cheminformatics. This approach employs abstract descriptions of topology and properties for rapid analyses and comparison.
Weisel, Martin, G Schneider, Kriegl, Jan M., M Weisel, Schneider, Gisbert (Prof. Dr.), J Kriegl   +5 more
core   +1 more source

Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning [PDF]

, 2020
International audienceGraph-based semi-supervised learning is appealing when labels are scarce but large amounts of unlabeled data are available.
Vargas-Vieyra, Mariana, Denis, Pascal, Bellet, Aurélien   +2 more
core   +2 more sources

Assessment instrument of graph representations on sound wave topic: Development and measurement implementation

Knowledge Management & E-Learning: An International Journal
Sound waves are one of the important topics studied in physics. However, students’ graph representation is still low, leading to their low concept understanding of physics learning.
Pramudya Wahyu Pradana, Supahar
doaj   +1 more source

Structure-Preserving Graph Representation Learning

2022 IEEE International Conference on Data Mining (ICDM), 2022
Accepted by the IEEE International Conference on Data Mining (ICDM) 2022.
Ruiyi Fang, Liangjian Wen, Zhao Kang 0001, Jianzhuang Liu   +3 more
openaire   +2 more sources