Results 21 to 30 of about 287,840 (285)

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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Review of Graph Embedding Learning Research:From Simple Graph to Complex Graph [PDF]

Jisuanji kexue
Graph 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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Synset2Node: A new synset embedding based upon graph embeddings

Intelligent Systems with Applications, 2023
Due to the advances made in recent years, embedding methods caused a significant increase in the accuracy of text or graph processing methods. Embedding methods exhibit a compact vector representation of the basic elements (words, synsets, nodes,..) of ...
Fatemeh Jafarinejad
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Knowledge Graph Embedding Model with Entity Description on Cement Manufacturing Domain [PDF]

Jisuanji kexue
To 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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Review of Research Progress on Knowledge Graph Embedding [PDF]

Jisuanji gongcheng
With the continuous development of big data and artificial intelligence technologies, knowledge graph embedding is developing rapidly, and knowledge graph applications are becoming increasingly widespread.
MA Hengzhi, QIAN Yurong, LENG Hongyong, WU Haipeng, TAO Wenbin, ZHANG Yiyang
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Graph Embedding With Data Uncertainty [PDF]

IEEE Access, 2022
20 pages, 4 ...
Laakom, Firas, Raitoharju, Jenni, Passalis, Nikolaos, Iosifidis, Alexandros, Gabbouj, Moncef   +5 more
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Graph Embedding Matrix Sharing With Differential Privacy

IEEE Access, 2019
Graph embedding maps a graph into low-dimensional vectors, i.e., embedding matrix, while preserving the graph structure, solving the high computation and space cost for graph analysis.
Sen Zhang, Weiwei Ni
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Extremal embedded graphs

Ars Mathematica Contemporanea, 2019
Summary: Let \(G\) be a ribbon graph and \(\mu (G)\) be the number of components of the virtual link formed from \(G\) as a cellularly embedded graph via the medial construction. In this paper we first prove that \(\mu (G) \leq f(G) + \gamma (G)\), where \(f(G)\) and \(\gamma (G)\) are the number of boundary components and Euler genus of \(G ...
Jin, Xian'an, Yan, Qi
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Improved Skip-Gram Based on Graph Structure Information

Sensors, 2023
Applying the Skip-gram to graph representation learning has become a widely researched topic in recent years. Prior works usually focus on the migration application of the Skip-gram model, while Skip-gram in graph representation learning, initially ...
Xiaojie Wang, Haijun Zhao, Huayue Chen
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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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