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>

scholarly journals Graph representation learning: a survey

2020 ◽  
Vol 9 ◽  
Author(s):  
Fenxiao Chen ◽  
Yun-Cheng Wang ◽  
Bin Wang ◽  
C.-C. Jay Kuo
Keyword(s):  
Graph Embedding ◽  
Large Data ◽  
Representation Learning ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Data ◽  
Graph Properties ◽  
Wide Range ◽  
Regular Lattices ◽  
Low Dimensional

Abstract Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

scholarly journals Enhancing Both Efficiency and Representational Capability of Isomap by Extensive Landmark Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Dong Liang ◽  
Chen Qiao ◽  
Zongben Xu
Keyword(s):  
Manifold Learning ◽  
Computational Efficiency ◽  
New Method ◽  
Data Sets ◽  
Learning Approaches ◽  
Real World Data ◽  
Efficiency Property ◽  
Data Points ◽  
Low Dimensional ◽  
High Computational Efficiency

The problems of improving computational efficiency and extending representational capability are the two hottest topics in approaches of global manifold learning. In this paper, a new method called extensive landmark Isomap (EL-Isomap) is presented, addressing both topics simultaneously. On one hand, originated from landmark Isomap (L-Isomap), which is known for its high computational efficiency property, EL-Isomap also possesses high computational efficiency through utilizing a small set of landmarks to embed all data points. On the other hand, EL-Isomap significantly extends the representational capability of L-Isomap and other global manifold learning approaches by utilizing only an available subset from the whole landmark set instead of all to embed each point. Particularly, compared with other manifold learning approaches, the data manifolds with intrinsic low-dimensional concave topologies and essential loops can be unwrapped by the new method more successfully, which are shown by simulation results on a series of synthetic and real-world data sets. Moreover, the accuracy, robustness, and computational complexity of EL-Isomap are analyzed in this paper, and the relation between EL-Isomap and L-Isomap is also discussed theoretically.

scholarly journals High-Dimensionality Graph Data Reduction Based on Proposing A New Algorithm

10.29007/h232 ◽  
2019 ◽  
Author(s):  
Lamyaa Al-Omairi ◽  
Jemal Abawajy ◽  
Morshed Chowdhury ◽  
Tahsien Al-Quraishi
Keyword(s):  
Dimensionality Reduction ◽  
Principal Component ◽  
Rayleigh Quotient ◽  
High Dimensionality ◽  
Data Sets ◽  
Graph Data ◽  
Reduction Methods ◽  
Massive Number ◽  
Modeling Data ◽  
Analysis System

In recent years, graph data analysis has become very important in modeling data distribution or structure in many applications, for example, social science, astronomy, computational biology or social networks with a massive number of nodes and edges. However, high-dimensionality of the graph data remains a difficult task, mainly because the analysis system is not used to dealing with large graph data. Therefore, graph-based dimensionality reduction approaches have been widely used in many machine learning and pattern recognition applications. This paper offers a novel dimensionality reduction approach based on the recent graph data. In particular, we focus on combining two linear methods: Neighborhood Preserving Embedding (NPE) method with the aim of preserving the local neighborhood information of a given dataset, and Principal Component Analysis (PCA) method with aims of maximizing the mutual information between the original high-dimensional data sets. The combination of NPE and PCA contributes to proposing a new Hybrid dimensionality reduction technique (HDR). We propose HDR to create a transformation matrix, based on formulating a generalized eigenvalue problem and solving it with Rayleigh Quotient solution. Consequently, therefore, a massive reduction is achieved compared to the use of PCA and NPE separately. We compared the results with the conventional PCA, NPE, and other linear dimension reduction methods. The proposed method HDR was found to perform better than other techniques. Experimental results have been based on two real datasets.

scholarly journals Adversarially Regularized Graph Autoencoder for Graph Embedding

2018 ◽  
Author(s):  
Shirui Pan ◽  
Ruiqi Hu ◽  
Guodong Long ◽  
Jing Jiang ◽  
Lina Yao ◽  
...  
Keyword(s):  
Real World ◽  
Topological Structure ◽  
Dimensional Space ◽  
Experimental Studies ◽  
Graph Embedding ◽  
Compact Representation ◽  
Graph Visualization ◽  
Graph Data ◽  
Training Scheme ◽  
Low Dimensional

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.  Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data,  but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in  real-world  graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding,  two variants of adversarial approaches,  adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction,  graph clustering, and graph visualization tasks.

scholarly journals Data Poisoning Attack against Knowledge Graph Embedding

2019 ◽  
Author(s):  
Hengtong Zhang ◽  
Tianhang Zheng ◽  
Jing Gao ◽  
Chenglin Miao ◽  
Lu Su ◽  
...  
Keyword(s):  
Question Answering ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Graph Data ◽  
Effectiveness And Efficiency ◽  
Significant Attention

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE's robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.

scholarly journals Dimensionality Reduction on Multi-Dimensional Transfer Functions for Multi-Channel Volume Data Sets

2010 ◽  
Vol 9 (3) ◽  
pp. 167-180 ◽  
Author(s):  
Han Suk Kim ◽  
Jürgen P. Schulze ◽  
Angela C. Cone ◽  
Gina E. Sosinsky ◽  
Maryann E. Martone
Keyword(s):  
Transfer Function ◽  
Dimensionality Reduction ◽  
Transfer Functions ◽  
Three Dimensions ◽  
New Method ◽  
Locally Linear Embedding ◽  
Nonlinear Dimensionality Reduction ◽  
Data Sets ◽  
Function Design ◽  
Transfer Function Design

The design of transfer functions for volume rendering is a non-trivial task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel, which require multi-dimensional transfer functions. In this article, we propose a new method for multi-dimensional transfer function design. Our new method provides a framework to combine multiple computational approaches and pushes the boundary of gradient-based multidimensional transfer functions to multiple channels, while keeping the dimensionality of transfer functions at a manageable level, that is, a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. Applying recently developed nonlinear dimensionality reduction algorithms reduce the high-dimensional data of the domain. In this article, we use Isomap and Locally Linear Embedding as well as a traditional algorithm, Principle Component Analysis. Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. We demonstrate the effectiveness of our new dimensionality reduction algorithms with two volumetric confocal microscopy data sets.

scholarly journals A New Method for Landmark-Based Studies of the Dynamic Stability of Growth, with Implications for Evolutionary Analyses

2021 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Growth Regulation ◽  
Pattern Analysis ◽  
Developmental Time ◽  
Dynamical Stability ◽  
New Method ◽  
Data Sets ◽  
Data Set ◽  
Landmark Data ◽  
Study Designs ◽  
Changes Over Time

AbstractA matrix manipulation new to the quantitative study of develomental stability reveals unexpected morphometric patterns in a classic data set of landmark-based calvarial growth. There are implications for evolutionary studies. Among organismal biology’s fundamental postulates is the assumption that most aspects of any higher animal’s growth trajectories are dynamically stable, resilient against the types of small but functionally pertinent transient perturbations that may have originated in genotype, morphogenesis, or ecophenotypy. We need an operationalization of this axiom for landmark data sets arising from longitudinal data designs. The present paper introduces a multivariate approach toward that goal: a method for identification and interpretation of patterns of dynamical stability in longitudinally collected landmark data. The new method is based in an application of eigenanalysis unfamiliar to most organismal biologists: analysis of a covariance matrix of Boas coordinates (Procrustes coordinates without the size standardization) against their changes over time. These eigenanalyses may yield complex eigenvalues and eigenvectors (terms involving $$i=\sqrt{-1}$$ i = - 1 ); the paper carefully explains how these are to be scattered, gridded, and interpreted by their real and imaginary canonical vectors. For the Vilmann neurocranial octagons, the classic morphometric data set used as the running example here, there result new empirical findings that offer a pattern analysis of the ways perturbations of growth are attenuated or otherwise modified over the course of developmental time. The main finding, dominance of a generalized version of dynamical stability (negative autoregressions, as announced by the negative real parts of their eigenvalues, often combined with shearing and rotation in a helpful canonical plane), is surprising in its strength and consistency. A closing discussion explores some implications of this novel pattern analysis of growth regulation. It differs in many respects from the usual way covariance matrices are wielded in geometric morphometrics, differences relevant to a variety of study designs for comparisons of development across species.

A New Method for Characterizing Replacement Rate Variation in Molecular Sequences: Application of the Fourier and Wavelet Models to Drosophila and Mammalian Proteins

Genetics ◽  
2000 ◽  
Vol 154 (1) ◽  
pp. 381-395
Author(s):  
Pavel Morozov ◽  
Tatyana Sitnikova ◽  
Gary Churchill ◽  
Francisco José Ayala ◽  
Andrey Rzhetsky
Keyword(s):  
Wavelet Transforms ◽  
Parametric Bootstrap ◽  
Real Data ◽  
New Method ◽  
Rate Variation ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Ratio Test ◽  
Replacement Rate ◽  
New Models

Abstract We propose models for describing replacement rate variation in genes and proteins, in which the profile of relative replacement rates along the length of a given sequence is defined as a function of the site number. We consider here two types of functions, one derived from the cosine Fourier series, and the other from discrete wavelet transforms. The number of parameters used for characterizing the substitution rates along the sequences can be flexibly changed and in their most parameter-rich versions, both Fourier and wavelet models become equivalent to the unrestricted-rates model, in which each site of a sequence alignment evolves at a unique rate. When applied to a few real data sets, the new models appeared to fit data better than the discrete gamma model when compared with the Akaike information criterion and the likelihood-ratio test, although the parametric bootstrap version of the Cox test performed for one of the data sets indicated that the difference in likelihoods between the two models is not significant. The new models are applicable to testing biological hypotheses such as the statistical identity of rate variation profiles among homologous protein families. These models are also useful for determining regions in genes and proteins that evolve significantly faster or slower than the sequence average. We illustrate the application of the new method by analyzing human immunoglobulin and Drosophilid alcohol dehydrogenase sequences.

scholarly journals ON GENERATING DIGITAL ELEVATION MODELS FROM LIDAR DATA – RESOLUTION VERSUS ACCURACY AND TOPOGRAPHIC WETNESS INDEX INDICES IN NORTHERN PEATLANDS

2012 ◽  
Vol 38 (2) ◽  
pp. 57-69 ◽  
Author(s):  
Abdulghani Hasan ◽  
Petter Pilesjö ◽  
Andreas Persson
Keyword(s):  
Large Scale ◽  
Drainage Area ◽  
Data Sets ◽  
Topographic Wetness Index ◽  
Absolute Deviation ◽  
Digital Elevation ◽  
Elevation Data ◽  
Scale Modelling ◽  
Data Points ◽  
Emission Modelling

Global change and GHG emission modelling are dependent on accurate wetness estimations for predictions of e.g. methane emissions. This study aims to quantify how the slope, drainage area and the TWI vary with the resolution of DEMs for a flat peatland area. Six DEMs with spatial resolutions from 0.5 to 90 m were interpolated with four different search radiuses. The relationship between accuracy of the DEM and the slope was tested. The LiDAR elevation data was divided into two data sets. The number of data points facilitated an evaluation dataset with data points not more than 10 mm away from the cell centre points in the interpolation dataset. The DEM was evaluated using a quantile-quantile test and the normalized median absolute deviation. It showed independence of the resolution when using the same search radius. The accuracy of the estimated elevation for different slopes was tested using the 0.5 meter DEM and it showed a higher deviation from evaluation data for steep areas. The slope estimations between resolutions showed differences with values that exceeded 50%. Drainage areas were tested for three resolutions, with coinciding evaluation points. The model ability to generate drainage area at each resolution was tested by pair wise comparison of three data subsets and showed differences of more than 50% in 25% of the evaluated points. The results show that consideration of DEM resolution is a necessity for the use of slope, drainage area and TWI data in large scale modelling.

scholarly journals Inter- and Intralaboratory Comparison of JC Polyomavirus Antibody Testing Using Two Different Virus-Like Particle-Based Assays

2014 ◽  
Vol 21 (11) ◽  
pp. 1581-1588 ◽  
Author(s):  
Piotr Kardas ◽  
Mohammadreza Sadeghi ◽  
Fabian H. Weissbach ◽  
Tingting Chen ◽  
Lea Hedman ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Data Sets ◽  
Antibody Testing ◽  
Jc Polyomavirus ◽  
Virus Like Particle ◽  
Interlaboratory Variability ◽  
Increased Risk ◽  
Basic Protocol ◽  
Data Points ◽  
Reference Serum

ABSTRACTJC polyomavirus (JCPyV) can cause progressive multifocal leukoencephalopathy (PML), a debilitating, often fatal brain disease in immunocompromised patients. JCPyV-seropositive multiple sclerosis (MS) patients treated with natalizumab have a 2- to 10-fold increased risk of developing PML. Therefore, JCPyV serology has been recommended for PML risk stratification. However, different antibody tests may not be equivalent. To study intra- and interlaboratory variability, sera from 398 healthy blood donors were compared in 4 independent enzyme-linked immunoassay (ELISA) measurements generating >1,592 data points. Three data sets (Basel1, Basel2, and Basel3) used the same basic protocol but different JCPyV virus-like particle (VLP) preparations and introduced normalization to a reference serum. The data sets were also compared with an independent method using biotinylated VLPs (Helsinki1). VLP preadsorption reducing ≥35% activity was used to identify seropositive sera. The results indicated that Basel1, Basel2, Basel3, and Helsinki1 were similar regarding overall data distribution (P= 0.79) and seroprevalence (58.0, 54.5, 54.8, and 53.5%, respectively;P= 0.95). However, intra-assay intralaboratory comparison yielded 3.7% to 12% discordant results, most of which were close to the cutoff (0.080 < optical density [OD] < 0.250) according to Bland-Altman analysis. Introduction of normalization improved overall performance and reduced discordance. The interlaboratory interassay comparison between Basel3 and Helsinki1 revealed only 15 discordant results, 14 (93%) of which were close to the cutoff. Preadsorption identified specificities of 99.44% and 97.78% and sensitivities of 99.54% and 95.87% for Basel3 and Helsinki1, respectively. Thus, normalization to a preferably WHO-approved reference serum, duplicate testing, and preadsorption for samples around the cutoff may be necessary for reliable JCPyV serology and PML risk stratification.

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