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Graph Embedding via Graph Summarization [PDF]
IEEE Access, 2021 Graph representation learning aims to represent the structural and semantic information of graph objects as dense real value vectors in low dimensional space by machine learning.
Jingyanning Yang +2 more
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Survey on Applications of Knowledge Graph Embedding in Recommendation Tasks [PDF]
Jisuanji kexue yu tansuo, 2022 Recommendation systems are designed to recommend personalized content to improve user experience. At present, the recommendation systems still face some challenges such as poor interpretability, cold start problem and serialized recommendation modeling ...
TIAN Xuan, CHEN Hangxue
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Advances in Knowledge Graph Embedding Based on Graph Neural Networks [PDF]
Jisuanji kexue yu tansuo, 2023 As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers.
YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
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Graph Embedding Models: A Survey [PDF]
Jisuanji kexue yu tansuo, 2022 Effective graph analysis methods can reveal the intrinsic characteristics of graph data. However, graph is non-Euclidean data, which leads to high computation and space cost while applying traditional methods.
YUAN Lining, LI Xin, WANG Xiaodong, LIU Zhao
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Proximity-Based Compression for Network Embedding
Frontiers in Big Data, 2021 Network embedding that encodes structural information of graphs into a low-dimensional vector space has been proven to be essential for network analysis applications, including node classification and community detection.
Muhammad Ifte Islam +4 more
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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.
Pereira, João +3 more
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Graph Embedding Framework Based on Adversarial and Random Walk Regularization
IEEE Access, 2021 Graph embedding aims to represent node structural as well as attribute information into a low-dimensional vector space so that some downstream application tasks such as node classification, link prediction, community detection, and recommendation can be ...
Wei Dou +3 more
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Fast Unsupervised Graph Embedding Based on Anchors [PDF]
Jisuanji kexue, 2022 Graph embedding is a widely used method for dimensionality reduction due to its computational effectiveness.The computational complexity of graph embedding method to construct traditional K-Nearest Neighbors (K-NN) graph is at least O(n2d), where n and d
YANG Hui, TAO Li-hong, ZHU Jian-yong, NIE Fei-ping
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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 ...
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Embedding Graphs into Colored Graphs [PDF]
Transactions of the American Mathematical Society, 1988 If X X is a graph, κ \kappa a cardinal, then there is a graph Y Y such that if the vertex set of Y Y is κ \kappa -colored, then there exists a monocolored induced copy of X X ; moreover, if X X does not contain a complete graph on
Hajnal, András, Komjáth, P.
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