A survey on visualization approaches for exploring association relationships in graph data

Chen, Yi; Guan, Zeli; Zhang, Rong; Du, Xuhua; Wang, Yunhai

doi:10.1007/s12650-019-00551-y

Cited by 30 publications

(27 citation statements)

References 86 publications

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“…Consequently, most surveys cover hypergraphvisualizations only as a sub-part -if at all -and rarely provide an extensive overview of the existing methodologies with respective advantages and drawbacks. Therefore, to compile a list of existing approaches, we also consider more generic surveys on set-and (regular) graph visualizations [2,6,10,26,40,41] which focus on relational aspects, as some aspects are comparable.…”

Section: Related Workmentioning

confidence: 99%

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Towards a Survey on Static and Dynamic Hypergraph Visualizations

Fischer,

Frings,

Keim

et al. 2021

Preprint

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Leveraging hypergraph structures to model advanced processes has gained much attention over the last few years in many areas, ranging from protein-interaction in computational biology to image retrieval using machine learning. Hypergraph models can provide a more accurate representation of the underlying processes while reducing the overall number of links compared to regular representations. However, interactive visualization methods for hypergraphs and hypergraph-based models have rarely been explored or systematically analyzed. This paper reviews the existing research landscape for hypergraph and hypergraph model visualizations and assesses the currently employed techniques. We provide an overview and a categorization of proposed approaches, focusing on performance, scalability, interaction support, successful evaluation, and the ability to represent different underlying data structures, including a recent demand for a temporal representation of interaction networks and their improvements beyond graph-based methods. Lastly, we discuss the strengths and weaknesses of the approaches and give an insight into the future challenges arising in this emerging research field.

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Section: Related Workmentioning

confidence: 99%

“…For a more set theory-focused approach, the survey [10] by Chen et al focuses on exploring association relationships in graphs. The authors propose a pipeline for visual analysis of associated data and summarized many different graph representations for large relationship data sets.…”

Section: Related Workmentioning

confidence: 99%

Towards a Survey on Static and Dynamic Hypergraph Visualizations

Fischer,

Frings,

Keim

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…By providing a visual interface which the user can interact with, the user is able to intuitively comprehend the data, perceive the underlying patterns (Lugmayr et al 2017) and query the data visually, without the need for programming knowledge. The ability for a user to interact with a visualisation makes visual exploration of the dataset possible and this has the major advantage of combining both human and machine intelligence (Chen et al 2019) to uncover unexpected and interesting phenomena within the dataset (Cho et al 2014). The benefits of visual analytics are "visual perception, interactive exploration, improved understanding, informed steering and intuitive interpretation" (Liu 2019).…”

Section: Interactivitymentioning

confidence: 99%

Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review

Lowe

Matthee

2020

Lecture Notes in Computer Science

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The continual growth of big data necessitates efficient ways of analysing these large datasets. Data visualisation and visual analytics has been identified as a key tool in big data analysis because they draw on the human visual and cognitive capabilities to analyse data quickly, intuitively and interactively. However, current visualisation tools and visual analytical systems fall short of providing a seamless user experience and several improvements could be made to current commercially available visualisation tools. By conducting a systematic literature review, requirements of visualisation tools were identified and categorised into six groups: dimensionality reduction, data reduction, scalability and readability, interactivity, fast retrieval of results, and user assistance. The most common themes found in the literature were dimensionality reduction and interactive data exploration.

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“…[ 6 ]. But in these developments, the application of new techniques is based on the same topology under the same topology, with a hodge-podge of similarities; we guess that if we analogize the training method of human brain information transfer and fundamentally change the network structure, the neural network constructed by this idea should have a better training performance and be able to be used in the field of deep learning [ 7 ]. The neural network built by this idea should have better training performance and brain-like information transfer characteristics to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Data Interaction Process Based on Data Mining and Neural Network Topology Visualization

Dai¹

2022

Computational Intelligence and Neuroscience

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This paper addresses data mining and neural network model construction and analysis to design a data interaction process model based on data mining and topology visualization. This paper performs preprocessing data operations such as data filtering and cleaning of the collected data. A typical multichannel convolutional neural network (MCNN) in deep learning techniques is applied to alert students’ academic performance. In addition, the network topology of the CNN is optimized to improve the performance of the model. The CNN has many hyperparameters that need to be tuned to construct an optimal model that can effectively interact with the data. In this paper, we propose a method to visualize the network topology within unstable regions to address the current problem of lacking an effective way to layout the network topology into specified areas. The technique transforms the network topology layout problem within the unstable region into a circular topology diffusion problem within a convex polygon, ensuring a clear, logical topology connection, and dramatically reducing the gaps in the area, making the layout more uniform beautiful. This paper constructs a real-time data interaction model based on JSON format and database triggers using message queues for reliable delivery. A platform-based real-time data interaction solution is designed by combining the timer method with the original key. The solution designed in this paper considers the real-time accuracy, security and reliability of data interaction. It satisfies the platform’s initial and newly discovered requirements for data interaction.

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A survey on visualization approaches for exploring association relationships in graph data

Cited by 30 publications

References 86 publications

Towards a Survey on Static and Dynamic Hypergraph Visualizations

Towards a Survey on Static and Dynamic Hypergraph Visualizations

Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review

Analysis of Data Interaction Process Based on Data Mining and Neural Network Topology Visualization

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