Euclidean Graphs and Trees

Finite Euclidean Graphs and Three Questions about Their Spectra

Fourier Analysis on Finite Groups and Applications ◽

10.1017/cbo9780511626265.007 ◽

1999 ◽

pp. 83-96

Keyword(s):

Euclidean Graphs

Approximating the Stretch Factor of Euclidean Graphs

Geometric Spanner Networks ◽

10.1017/cbo9780511546884.014 ◽

2009 ◽

pp. 242-254

Author(s):

Giri Narasimhan ◽

Michiel Smid

Keyword(s):

Stretch Factor ◽

Euclidean Graphs

Approximating the Stretch Factor of Euclidean Graphs

SIAM Journal on Computing ◽

10.1137/s0097539799361671 ◽

2000 ◽

Vol 30 (3) ◽

pp. 978-989 ◽

Cited By ~ 41

Author(s):

Giri Narasimhan ◽

Michiel Smid

Keyword(s):

Stretch Factor ◽

Euclidean Graphs

Some characterization, uniqueness and existence results for Euclidean graphs of constant mean curvature with planar boundary

Pacific Journal of Mathematics ◽

10.2140/pjm.2001.198.175 ◽

2001 ◽

Vol 198 (1) ◽

pp. 175-196 ◽

Cited By ~ 8

Author(s):

Jaime Ripoll

Keyword(s):

Mean Curvature ◽

Constant Mean Curvature ◽

Existence Results ◽

Planar Boundary ◽

Uniqueness And Existence ◽

Euclidean Graphs ◽

Uniqueness And Existence Results

Shortest Paths In Euclidean Graphs

25th Annual Symposium onFoundations of Computer Science, 1984. ◽

10.1109/sfcs.1984.715943 ◽

2005 ◽

Cited By ~ 1

Author(s):

R. Sedgewick ◽

J.S. Vitter

Keyword(s):

Shortest Paths ◽

Euclidean Graphs

Finite Euclidean graphs and Ramanujan graphs

Discrete Mathematics ◽

10.1016/j.disc.2009.06.008 ◽

2009 ◽

Vol 309 (20) ◽

pp. 6126-6134 ◽

Cited By ~ 6

Author(s):

Eiichi Bannai ◽

Osamu Shimabukuro ◽

Hajime Tanaka

Keyword(s):

Ramanujan Graphs ◽

Euclidean Graphs

Strong laws for Euclidean graphs with general edge weights

Statistics & Probability Letters ◽

10.1016/s0167-7152(01)00175-4 ◽

2002 ◽

Vol 56 (3) ◽

pp. 251-259 ◽

Cited By ~ 4

Author(s):

Raúl Jiménez ◽

J.E. Yukich

Keyword(s):

Strong Laws ◽

Euclidean Graphs

Statistical Distances Based on Euclidean Graphs

Recent Advances in Applied Probability ◽

10.1007/0-387-23394-6_10 ◽

2006 ◽

pp. 223-239 ◽

Cited By ~ 1

Author(s):

R. Jiménez ◽

J. E. Yukich

Keyword(s):

Euclidean Graphs

A genetic approach to hierarchical clustering of Euclidean graphs

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170) ◽

10.1109/icpr.1998.712002 ◽

2002 ◽

Cited By ~ 2

Author(s):

S. Rizzi

Keyword(s):

Hierarchical Clustering ◽

Genetic Approach ◽

Euclidean Graphs

Geometric Deep Lean Learning: Evaluation Using a Twitter Social Network

Applied Sciences ◽

10.3390/app11156777 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6777

Author(s):

Javier Villalba-Diez ◽

Martin Molina ◽

Daniel Schmidt

Keyword(s):

Social Network ◽

Link Prediction ◽

Industry 4.0 ◽

Learning Algorithm ◽

High Accuracy ◽

Topological Evolution ◽

Deep Learning Algorithm ◽

Learning Evaluation ◽

Essential Form ◽

Euclidean Graphs

The goal of this work is to evaluate a deep learning algorithm that has been designed to predict the topological evolution of dynamic complex non-Euclidean graphs in discrete–time in which links are labeled with communicative messages. This type of graph can represent, for example, social networks or complex organisations such as the networks associated with Industry 4.0. In this paper, we first introduce the formal geometric deep lean learning algorithm in its essential form. We then propose a methodology to systematically mine the data generated in social media Twitter, which resembles these complex topologies. Finally, we present the evaluation of a geometric deep lean learning algorithm that allows for link prediction within such databases. The evaluation results show that this algorithm can provide high accuracy in the link prediction of a retweet social network.