Network Visualization Survey

Komarek, Ales; Pavlik, Jakub; Soběslav, Vladimír

doi:10.1007/978-3-319-24306-1_27

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…To do so, it is interesting to investigate the changes in roles during the different phases of the course. Therefore we provide a longitudinal study through a Sankey diagram, a visualization tool allowing to describe node flows between different steps [15]. Figure 2 depicts the evolution of roles (each color line) within the phases (each vertical line) of one of the courses.…”

Section: Role Characterizationmentioning

confidence: 99%

Roles in Social Interactions: Graphlets in Temporal Networks Applied to Learning Analytics

Charbey

Brisson

Bothorel

et al. 2019

Complex Networks and Their Applications VIII

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There is a growing interest in how data generated in learning platforms, especially the interaction data, can be used to improve teaching and learning. Social network analysis and machine learning methods take advantage of network topology to detect relational patterns and model interaction behaviors. Specifically, small induced subgraphs called graphlets, provide an efficient topological description of the way each node is embedded in the meso-scale structure of a network. Here we propose to detect the roles occupied by the different participants, students and teachers, in the successive phases of courses modeled by a sequence of static snapshots. The detected positions, obtained thanks to graphlet enumeration combined with a clustering method, reveal the different roles observed in each snapshot. We also track the role changes through the overall sequence of snapshots. We apply our method to the Sqily platform and describe the mutual skill validation process. The detected roles, the transitions between roles and a overall visualization through Sankey diagrams help interpreting the course dynamics. We found that some roles act like necessary steps to engage students within an active exchange process with their classmates.

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Section: Role Characterizationmentioning

confidence: 99%

Roles in Social Interactions: Graphlets in Temporal Networks Applied to Learning Analytics

Charbey

Brisson

Bothorel

et al. 2019

Complex Networks and Their Applications VIII

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“…It is hard to distinguish individual links and impossible to follow indirect interactions. Accumulation of links in the space between guilds creates what is known as the hairball effect [37], but the main shortcoming of the bipartite plot is that it does not show the network hierarchical organization.…”

Section: Introductionmentioning

confidence: 99%

A Structural Approach to Disentangle the Visualization of Bipartite Biological Networks

et al. 2018

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Interactions between two different guilds of entities are pervasive in biology. They may happen at molecular level, like in a diseasome, or amongst individuals linked by biotic relationships, such as mutualism or parasitism. These sets of interactions are complex bipartite networks. Visualization is a powerful tool to explore and analyze them, but the most common plots, the bipartite graph and the interaction matrix, become rather confusing when working with real biological networks. We have developed two new types of visualization which exploit the structural properties of these networks to improve readability. A technique called k-core decomposition identifies groups of nodes that share connectivity properties. With the results of this analysis it is possible to build a plot based on information reduction (polar plot) and another which takes the groups as elementary blocks for spatial distribution (ziggurat plot). We describe the applications of both plots and the software to create them.

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“…It is hard to distinguish individual links and impossible to follow indirect interactions. Accumulation of links in the space between guilds creates what is known as the hairball effect [32], but the main shortcoming of the bipartite plot is that it does not show the network hyerarchical organisation.…”

Section: Introductionmentioning

confidence: 99%

A structural approach to disentangle the visualization of bipartite biological networks

García-Algarra

Pastor

López

et al. 2017

Preprint

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Interactions between two different guilds of entities are pervasive in biology. They may happen at molecular level, like in a diseasome, or amongst individuals linked by biotic relationships, such as mutualism or parasitism. These sets of interactions are complex bipartite networks. Visualization is a powerful tool to explore and analyse them but the most common plots, the bipartite graph and the interaction matrix, become rather confusing when working with real biological networks. We have developed two new types of visualization that exploit the structural properties of these networks to improve readability. A technique called k-core decomposition identifies groups of nodes that share connectivity properties. With the results of this analysis it is possible to build a plot based on information reduction (Polar Plot) and another which takes the groups as elementary blocks for spatial distribution (Ziggurat plot). We describe the applications of both plots and the software to create them.

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Network Visualization Survey

Cited by 13 publications

References 9 publications

Roles in Social Interactions: Graphlets in Temporal Networks Applied to Learning Analytics

Roles in Social Interactions: Graphlets in Temporal Networks Applied to Learning Analytics

A Structural Approach to Disentangle the Visualization of Bipartite Biological Networks

A structural approach to disentangle the visualization of bipartite biological networks

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