Results 21 to 30 of about 70,588 (313)
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
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Topological feature generation for link prediction in biological networks [PDF]
PeerJ, 2023 Graph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks.
Mustafa Temiz +3 more
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Attributed Graph Embedding Based on Attention with Cluster
Mathematics, 2022 Graph embedding is of great significance for the research and analysis of graphs. Graph embedding aims to map nodes in the network to low-dimensional vectors while preserving information in the original graph of nodes.
Bin Wang +3 more
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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
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
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Review of Graph Embedding Learning Research:From Simple Graph to Complex Graph [PDF]
Jisuanji kexueGraph data,as a data type with strong expressive power,is difficult to model efficiently due to its complex structure.How to effectively capture its intrinsic information has become a challenging problem.Graph embedding methods have received increasing ...
HUANG Miaomiao, WANG Huiying, WANG Meixia, WANG Yejiang , ZHAO Yuhai
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WGEVIA: A Graph Level Embedding Method for Microcircuit Data
Frontiers in Computational Neuroscience, 2021 Functional microcircuits are useful for studying interactions among neural dynamics of neighboring neurons during cognition and emotion. A functional microcircuit is a group of neurons that are spatially close, and that exhibit synchronized neural ...
Xiaomin Wu +4 more
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Knowledge Graph Embedding Model with Entity Description on Cement Manufacturing Domain [PDF]
Jisuanji kexueTo address the problem that many knowledge graph embedding models lack the consideration of semantic information when performing knowledge embedding and cannot extract the semantic information of entities specialized in cement manufactu-ring domain well ...
ZHOU Honglin, SONG Huazhu, ZHANG Juan
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Recommender Systems Based on Graph Embedding Techniques: A Review
IEEE Access, 2022 As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user’s preferred items from millions of candidates by analyzing observed user-item relations.
Yue Deng
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Genus Distribution for a Graph [PDF]
, 2009 In this paper we develop the technique of a distribution decomposition for a graph. A formula is given to determine genus distribution of a cubic graph.
Liangxia, Wan +2 more
core +1 more source