Graph Embedding: A General Framework for Dimensionality Reduction

2005 ◽  
Author(s):  
Shuicheng Yan ◽  
Dong Xu ◽  
Benyu Zhang ◽  
Hong-Jiang Zhang
Keyword(s):  
Dimensionality Reduction ◽  
General Framework ◽  
Graph Embedding

scholarly journals Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

2007 ◽  
Vol 29 (1) ◽  
pp. 40-51 ◽  
Author(s):  
Shuicheng Yan ◽  
Dong Xu ◽  
Benyu Zhang ◽  
Hong-jiang Zhang ◽  
Qiang Yang ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
General Framework ◽  
Graph Embedding

scholarly journals Sparsity and Geometry Preserving Graph Embedding for Dimensionality Reduction

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 75748-75766 ◽  
Author(s):  
Jianping Gou ◽  
Zhang Yi ◽  
David Zhang ◽  
Yongzhao Zhan ◽  
Xiangjun Shen ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Graph Embedding

Discriminative globality and locality preserving graph embedding for dimensionality reduction

2020 ◽  
Vol 144 ◽  
pp. 113079 ◽  
Author(s):  
Jianping Gou ◽  
Yuanyuan Yang ◽  
Zhang Yi ◽  
Jiancheng Lv ◽  
Qirong Mao ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Graph Embedding ◽  
Locality Preserving

scholarly journals More faithfulness graph embedding

2015 ◽  
Vol 4 (2) ◽  
pp. 336
Author(s):  
Alaa Najim
Keyword(s):  
Dimensionality Reduction ◽  
Graph Embedding ◽  
New Method ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Visualization ◽  
Graph Data ◽  
Original Space ◽  
Data Points ◽  
Effectiveness And Efficiency

<p><span lang="EN-GB">Using dimensionality reduction idea to visualize graph data sets can preserve the properties of the original space and reveal the underlying information shared among data points. Continuity Trustworthy Graph Embedding (CTGE) is new method we have introduced in this paper to improve the faithfulness of the graph visualization. We will use CTGE in graph field to find new understandable representation to be more easy to analyze and study. Several experiments on real graph data sets are applied to test the effectiveness and efficiency of the proposed method, which showed CTGE generates highly faithfulness graph representation when compared its representation with other methods.</span></p>

A generalized least-squares approach regularized with graph embedding for dimensionality reduction

2020 ◽  
Vol 98 ◽  
pp. 107023 ◽  
Author(s):  
Xiang-Jun Shen ◽  
Si-Xing Liu ◽  
Bing-Kun Bao ◽  
Chun-Hong Pan ◽  
Zheng-Jun Zha ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Least Squares ◽  
Graph Embedding ◽  
Generalized Least Squares ◽  
Least Squares Approach

scholarly journals An unsupervised framework for comparing graph embeddings

2019 ◽  
Author(s):  
Bogumił Kamiński ◽  
Paweł Prałat ◽  
François Théberge
Keyword(s):  
Real World ◽  
General Framework ◽  
Graph Embedding ◽  
Relevant Information ◽  
Graph Embeddings ◽  
Graph Topology ◽  
Domain Experts ◽  
Main Challenge ◽  
Synthetic Networks

Abstract Graph embedding is the transformation of vertices of a graph into set of vectors. A good embedding should capture the graph topology, vertex-to-vertex relationship and other relevant information about the graph, its subgraphs and vertices. If these objectives are achieved, an embedding is a meaningful, understandable and compressed representations of a network. Finally, vector operations are simpler and faster than comparable operations on graphs. The main challenge is that one needs to make sure that embeddings well describe the properties of the graphs. In particular, a decision has to be made on the embedding dimensionality which highly impacts the quality of an embedding. As a result, selecting the best embedding is a challenging task and very often requires domain experts. In this article, we propose a ‘divergence score’ that can be assigned to embeddings to help distinguish good ones from bad ones. This general framework provides a tool for an unsupervised graph embedding comparison. In order to achieve it, we needed to generalize the well-known Chung-Lu model to incorporate geometry which is an interesting result in its own right. In order to test our framework, we did a number of experiments with synthetic networks as well as real-world networks, and various embedding algorithms.

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