Results 31 to 40 of about 70,588 (313)
Review of Research Progress on Knowledge Graph Embedding [PDF]
Jisuanji gongchengWith the continuous development of big data and artificial intelligence technologies, knowledge graph embedding is developing rapidly, and knowledge graph applications are becoming increasingly widespread.
MA Hengzhi, QIAN Yurong, LENG Hongyong, WU Haipeng, TAO Wenbin, ZHANG Yiyang
doaj +1 more source
Synset2Node: A new synset embedding based upon graph embeddings
Intelligent Systems with Applications, 2023 Due to the advances made in recent years, embedding methods caused a significant increase in the accuracy of text or graph processing methods. Embedding methods exhibit a compact vector representation of the basic elements (words, synsets, nodes,..) of ...
Fatemeh Jafarinejad
doaj +1 more source
Graph Embedding With Data Uncertainty [PDF]
IEEE Access, 2022 20 pages, 4 ...
Laakom, Firas +5 more
openaire +7 more sources
Graph Embedding Matrix Sharing With Differential Privacy
IEEE Access, 2019 Graph embedding maps a graph into low-dimensional vectors, i.e., embedding matrix, while preserving the graph structure, solving the high computation and space cost for graph analysis.
Sen Zhang, Weiwei Ni
doaj +1 more source
Isometric embeddings of graphs [PDF]
Proceedings of the National Academy of Sciences, 1984 We prove that any finite undirected graph can be canonically embedded isometrically into a maximum cartesian product of irreducible factors.
Graham, R. L., Winkler, P. M.
openaire +3 more sources
Knowledge Graph Embedding by Dynamic Translation [PDF]
, 2017 Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors. It can efficiently measure semantic correlations of entities and relations in knowledge graphs, and improve the ...
Tianlong Gu +11 more
core +1 more source
A Toroidal Maxwell–Cremona–Delaunay Correspondence
Journal of Computational Geometry, 2022 We consider three classes of geodesic embeddings of graphs on Euclidean flat tori: • A toroidal graph embedding Γ is positive equilibrium if it is possible to place positive weights on the edges, such that the weighted edge vectors incident to each ...
Jeff Erickson, Patrick Lin
doaj +1 more source
Adversarially regularized graph autoencoder for graph embedding [PDF]
, 2018 © 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
Jiang, Jing +17 more
core +1 more source
Improved Skip-Gram Based on Graph Structure Information
Sensors, 2023 Applying the Skip-gram to graph representation learning has become a widely researched topic in recent years. Prior works usually focus on the migration application of the Skip-gram model, while Skip-gram in graph representation learning, initially ...
Xiaojie Wang, Haijun Zhao, Huayue Chen
doaj +1 more source
Patch alignment for graph embedding [PDF]
, 2012 © Springer Science+Business Media New York 2013. Dozens of manifold learning-based dimensionality reduction algorithms have been proposed in the literature.
Dacheng Tao +5 more
core +1 more source