Results 11 to 20 of about 70,588 (313)
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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Link-Privacy Preserving Graph Embedding Data Publication with Adversarial Learning
Tsinghua Science and Technology, 2022 The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods, aiming at learning a continuous vector space for the graph, which is amenable to be adopted in traditional machine learning ...
Kainan Zhang +3 more
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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 a Forest in a Graph [PDF]
The Electronic Journal of Combinatorics, 2011 For $p\ge 1$, we prove that every forest with $p$ trees whose sizes are $a_1, \ldots, a_p$ can be embedded in any graph containing at least $\sum_{i=1}^p (a_i + 1)$ vertices and having minimum degree at least $\sum_{i=1}^p a_i$.
Mark K. Goldberg, Malik Magdon-Ismail
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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.
João P. B. Pereira +3 more
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SAGES: Scalable Attributed Graph Embedding With Sampling for Unsupervised Learning [PDF]
, 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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On Embeddings of Circulant Graphs [PDF]
The Electronic Journal of Combinatorics, 2015 A circulant of order $n$ is a Cayley graph for the cyclic group $\mathbb{Z}_n$, and as such, admits a transitive action of $\mathbb{Z}_n$ on its vertices. This paper concerns 2-cell embeddings of connected circulants on closed orientable surfaces.
Conder, Marston, Grande, Ricardo
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whole-graph-embedding-us-city-polycentricity [PDF]
, 2022 Conventional polycentricity scores and embedding vectors of US ...
Cheng Fu (8354925)
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GAE-Based Document Embedding Method for Clustering
IEEE Access, 2022 Document embedding methods for clustering using deep neural networks have been proposed recently. However, the existing deep neural network-based document embedding methods for clustering have a problem of either generating document embeddings dependent ...
Sungwon Jung, Sangmin Ka
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Joint Embedding of Graphs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important applications in statistical inference on graphs. We propose a method to jointly embed multiple undirected graphs.
Shangsi Wang +3 more
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