Dimension reduction methods in graph drawing problem

Zaorálek, Lukáš; Burianek, Tomas; Snášel, Václav

doi:10.1109/isda.2014.7066252

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Koren et al [33] improved HDE by replacing PCA with subspace optimization. Zaorálek et al [67] compared several different dimensionality reduction methods for graph layout. More recently, powerful deep neural networks are also utilized to learn how to draw a graph from training examples [37,60].…”

Section: Graph Layoutmentioning

confidence: 99%

DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction

Zhu

Chen

et al. 2020

Preprint

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Efficient layout of large-scale graphs remains a challenging problem: the force-directed and dimensionality reduction-based methods suffer from high overhead for graph distance and gradient computation. In this paper, we present a new graph layout algorithm, called DRGraph, that enhances the nonlinear dimensionality reduction process with three schemes: approximating graph distances by means of a sparse distance matrix, estimating the gradient by using the negative sampling technique, and accelerating the optimization process through a multi-level layout scheme. DRGraph achieves a linear complexity for the computation and memory consumption, and scales up to large-scale graphs with millions of nodes. Experimental results and comparisons with state-of-the-art graph layout methods demonstrate that DRGraph can generate visually comparable layouts with a faster running time and a lower memory requirement.

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Section: Graph Layoutmentioning

confidence: 99%

DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction

Zhu

Chen

et al. 2020

Preprint

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An empirical analysis of graph‐based linear dimensionality reduction techniques

Al-Omairi

Abawajy

Chowdhury

et al. 2020

Concurrency and Computation

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Summary Many emerging applications such as social networks have prompted remarkable attention in graph data analysis. Graph data is typically high‐dimensional in nature, and dimensionality reduction is critical regarding storage, analysis, and querying of such data efficiently. Although there are many dimensionality reduction methods, it is not clear to what extent the performances of the various dimensionality reduction techniques differ. In this article, we review some of the well‐known linear dimensionality reduction methods and perform an empirical analysis of these approaches using large multidimensional graph datasets. Our results show that in linear unsupervised learning methods, the principal component analysis, singular value decomposition, and neighborhood preserving embedding methods achieve better retrieval data performance than other methods of the statistical information category, dictionary methods, and embedding methods, respectively. Regarding supervised learning methods, the experimental results demonstrate that linear discriminant analysis and partial least squares presented almost similar results.

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Optimizing graph layout by t-SNE perplexity estimation

Xiao

Hong

Huang

2022

Int J Data Sci Anal

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Perplexity is one of the key parameters of dimensionality reduction algorithm of t-distributed stochastic neighbor embedding (t-SNE). In this paper, we investigated the relationship of t-SNE perplexity and graph layout evaluation metrics including graph stress, preserved neighborhood information and visual inspection. As we found that a small perplexity is correlated with a relative higher normalized stress while preserving neighborhood information with a higher precision but less global structure information, we proposed our method to estimate appropriate perplexity either based on a modified standard t-SNE or the sklearn Barnes–Hut TSNE. Experimental results demonstrate effectiveness and ease of use of our approach when tested on a set of benchmark datasets.

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Dimension reduction methods in graph drawing problem

Cited by 4 publications

References 13 publications

DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction

DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction

An empirical analysis of graph‐based linear dimensionality reduction techniques

Optimizing graph layout by t-SNE perplexity estimation

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