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Uniform graph embedding into metric spaces [PDF]
The task of embedding an infinity countable graph into continuous metric space is considered. The concept of uniform embedding having no accumulation point in a set of vertex images and having all graph edge images of a limited length is introduced ...
A. V. Koganov
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Embedding into Bipartite Graphs [PDF]
16 pages, 2 ...
Böttcher, Julia +2 more
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Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Unsupervised graph embedding techniques aim
Stephen Bonner +5 more
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Enriching Translation-Based Knowledge Graph Embeddings Through Continual Learning
This paper addresses an enrichment of translation-based knowledge graph embeddings. When new knowledge triples become available after a knowledge graph is embedded onto a vector space, the embedding should be enriched with the new triples, but without ...
Hyun-Je Song, Seong-Bae Park
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Minor-Embedding in Adiabatic Quantum Computation: I. The Parameter Setting Problem [PDF]
We show that the NP-hard quadratic unconstrained binary optimization (QUBO) problem on a graph $G$ can be solved using an adiabatic quantum computer that implements an Ising spin-1/2 Hamiltonian, by reduction through minor-embedding of $G$ in the quantum
Choi, Vicky
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Attention-Aware Heterogeneous Graph Neural Network
As a powerful tool for elucidating the embedding representation of graph-structured data, Graph Neural Networks (GNNs), which are a series of powerful tools built on homogeneous networks, have been widely used in various data mining tasks.
Jintao Zhang, Quan Xu
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Isometric embeddings of graphs [PDF]
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.
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Learning Graph Embedding with Adversarial Training Methods
Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction ...
Fung, Sai-fu +5 more
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Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design
In [Choi08], we introduced the notion of minor-embedding in adiabatic quantum optimization. A minor-embedding of a graph G in a quantum hardware graph U is a subgraph of U such that G can be obtained from it by contracting edges.
Choi, Vicky
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Spectral embedding of graphs [PDF]
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luo, Bin +2 more
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