Results 11 to 20 of about 845,204 (303)
Spectral Geometry for Structural Pattern Recognition [PDF]
, 2010 Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis
El Ghawalby, Hewayda
core +6 more sources
Embedding Graphs into Embedded Graphs [PDF]
Algorithmica, 2020 A (possibly denerate) drawing of a graph $G$ in the plane is approximable by an embedding if it can be turned into an embedding by an arbitrarily small perturbation. We show that testing, whether a straight-line drawing of a planar graph $G$ in the plane is approximable by an embedding, can be carried out in polynomial time, if a desired embedding of ...
openaire +6 more sources
Embedding a Forest in a Graph [PDF]
The Electronic Journal of Combinatorics, 2011 For $p\ge 1$, we prove that every forest with $p$ trees whose sizes are $a_1, \ldots, a_p$ can be embedded in any graph containing at least $\sum_{i=1}^p (a_i + 1)$ vertices and having minimum degree at least $\sum_{i=1}^p a_i$.
Mark K. Goldberg, Malik Magdon-Ismail
openaire +4 more sources
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required. We provide theoretical results on an optimal regime for the GSE, namely a feasibility region for its parameters, and demonstrate the experimental relevance of our findings.
João P. B. Pereira +3 more
openaire +3 more sources
On Embeddings of Circulant Graphs [PDF]
The Electronic Journal of Combinatorics, 2015 A circulant of order $n$ is a Cayley graph for the cyclic group $\mathbb{Z}_n$, and as such, admits a transitive action of $\mathbb{Z}_n$ on its vertices. This paper concerns 2-cell embeddings of connected circulants on closed orientable surfaces.
Conder, Marston, Grande, Ricardo
openaire +4 more sources
GAE-Based Document Embedding Method for Clustering
IEEE Access, 2022 Document embedding methods for clustering using deep neural networks have been proposed recently. However, the existing deep neural network-based document embedding methods for clustering have a problem of either generating document embeddings dependent ...
Sungwon Jung, Sangmin Ka
doaj +1 more source
Joint Embedding of Graphs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important applications in statistical inference on graphs. We propose a method to jointly embed multiple undirected graphs.
Shangsi Wang +3 more
openaire +3 more sources
JONNEE: Joint Network Nodes and Edges Embedding
IEEE Access, 2021 Recently, graph embedding models significantly improved the quality of graph machine learning tasks, such as node classification and link prediction. In this work, we propose a model called JONNEE (JOint Network Nodes and Edges Embedding), which learns ...
Ilya Makarov +2 more
doaj +1 more source
Graph Representation Learning and Its Applications: A Survey
Sensors, 2023 Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc.
Van Thuy Hoang +5 more
doaj +1 more source
Corrigendum to "Embedding Graphs into Colored Graphs" [PDF]
Transactions of the American Mathematical Society, 1992 The authors give a corrected exposition of the proof of Theorem 12 of their quoted paper [ibid. 307, No. 1, 395-409 (1988; Zbl 0659.03029)].
Hajnal, András, Komjáth, P.
openaire +2 more sources