Visualizing large knowledge graphs: A performance analysis

Gómez‐Romero, Juan; Molina-Solana, Miguel; Oehmichen, Axel; Guo, Yike

doi:10.1016/j.future.2018.06.015

Cited by 23 publications

(12 citation statements)

References 47 publications

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“…When large networks are considered, the number of links might become an issue for visualization, resulting in the dreaded "hairball". Current approaches on graph layouts [38] could manage up to a million nodes if the network is sparse [58,46]. Consequently, additional transformations such as aggregation to reduce the number of nodes and/or edges are still needed.…”

Section: Visual Scalabilitymentioning

confidence: 99%

Spanning Trees as Approximation of Data Structures

Alcaide

Aerts

2021

IEEE Trans. Visual. Comput. Graphics

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The connections in a graph generate a structure that is independent of a coordinate system. This visual metaphor allows creating a more flexible representation of data than a two-dimensional scatterplot. In this work, we present STAD (Simplified Topological Abstraction of Data), a parameter-free dimensionality reduction method that projects high-dimensional data into a graph. STAD generates an abstract representation of high-dimensional data by giving each data point a location in a graph which preserves the approximate distances in the original high-dimensional space. The STAD graph is built upon the Minimum Spanning Tree (MST) to which new edges are added until the correlation between the distances from the graph and the original dataset is maximized. Additionally, STAD supports the inclusion of additional functions to focus the exploration and allow the analysis of data from new perspectives, emphasizing traits in data which otherwise would remain hidden. We demonstrate the effectiveness of our method by applying it to two real-world datasets: traffic density in Barcelona and temporal measurements of air quality in Castile and León in Spain.

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Section: Visual Scalabilitymentioning

confidence: 99%

Spanning Trees as Approximation of Data Structures

Alcaide

Aerts

2021

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…When large networks are considered, the number of links might become an issue for visualization, resulting in the dreaded "hairball". Current approaches on graph layouts [36] could manage up to a million nodes if the network is sparse [54,44]. Consequently, additional transformations such as aggregation to reduce the number of nodes and/or edges are still needed.…”

Section: Visual Scalabilitymentioning

confidence: 99%

Improving the Projection of Global Structures in Data through Spanning Trees

Alcaide¹,

Aerts²

2019

Preprint

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The connection of edges in a graph generates a structure that is independent of a coordinate system. This visual metaphor allows creating a more flexible representation of data than a two-dimensional scatterplot. In this work, we present STAD (Spanning Trees as Approximation of Data), a dimensionality reduction method to approximate the high-dimensional structure into a graph with or without formulating prior hypotheses. STAD generates an abstract representation of high-dimensional data by giving each data point a location in a graph which preserves the distances in the original high-dimensional space. The STAD graph is built upon the Minimum Spanning Tree (MST) to which new edges are added until the correlation between the distances from the graph and the original dataset is maximized. Additionally, STAD supports the inclusion of additional functions to focus the exploration and allow the analysis of data from new perspectives, emphasizing traits in data which otherwise would remain hidden. We demonstrate the effectiveness of our method by applying it to two real-world datasets: traffic density in Barcelona and temporal measurements of air quality in Castile and Le ón in Spain.

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“…For example, it may preserve the local neighborhood information of each node as well as global community structure of the network. Therefore, the node representations can be used as features for network analysis and network prediction tasks such as classification [7], clustering [8,9], link prediction [10,11], and visualization [12,13].…”

Section: Introductionmentioning

confidence: 99%

Degree-biased random walk for large-scale network embedding

Zhang

Shi

Feng

et al. 2019

Future Generation Computer Systems

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h i g h l i g h t s• The current random walk based network embedding methods cannot well adapt to the scale-free feature of real-world networks.• A degree-biased random walk method is proposed to adapt to the scale-free feature of real-world networks and extract topological information as fully as possible for representation learning.• The space and computation overhead for network embedding can be drastically reduced by adopting by condensing the fixed-length sequences into variable-length ones based on nodes centrality.

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Visualizing large knowledge graphs: A performance analysis

Cited by 23 publications

References 47 publications

Spanning Trees as Approximation of Data Structures

Spanning Trees as Approximation of Data Structures

Improving the Projection of Global Structures in Data through Spanning Trees

Degree-biased random walk for large-scale network embedding

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