Results 41 to 50 of about 845,204 (303)
Scalable Robust Graph Embedding with Spark
, 2022 Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties.
Weidlich, Matthias +5 more
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Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Data Science and Engineering, 2019 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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Real-Time Semantic Data Flow Reasoning Based on Improved Multi-Embedding Space [PDF]
Jisuanji gongcheng, 2022 The joint use of semantic data flow processing engine and knowledge graph embedding representation learning can effectively improve the performance of real-time data stream reasoning and query.The existing knowledge representation learning models pay ...
GAO Feng, YAO Guangtao, GU Jinguang
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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.
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Enriching Translation-Based Knowledge Graph Embeddings Through Continual Learning
IEEE Access, 2018 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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SAGES: Scalable Attributed Graph Embedding With Sampling for Unsupervised Learning
, 2023 Unsupervised graph embedding method generates node embeddings to preserve structural and content features in a graph without human labeling burden. However, most unsupervised graph representation learning methods suffer issues like poor scalability or ...
Wang, Jialin +5 more
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Enumeration of graph embeddings
Discrete Mathematics, 1994 Let \(G\) be a finite connected simple graph, with \(\Gamma\) a subgroup of \(\Aut G\). Two 2-cell imbeddings \(i: G\to S\) and \(j: G\to S\) of \(G\) into a closed surface \(S\) (orientable or nonorientable) are said to be congruent with respect to \(\Gamma\) if there exist a surface homeomorphism \(h: S\to S\) and a \(\gamma\in \Gamma\) such that \(h\
KWAK, JH, LEE, J
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whole-graph-embedding-us-city-polycentricity
, 2022 Conventional polycentricity scores and embedding vectors of US ...
Cheng Fu (8354925)
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Discrete Mathematics, 1994 The author surveys some recent results about graphs embedded on higher surfaces or in general topological spaces. He begins by examining the relationship between the Jordan Curve Theorem and Kuratowski's Theorem. In particular, he discusses his result that in an arbitrary arcwise connected Hausdorff space which is not a simple closed curve and in which
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Attention-Aware Heterogeneous Graph Neural Network
Big Data Mining and Analytics, 2021 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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