Online force-directed algorithms for visualization of dynamic graphs

Cheong, Se-Hang; Si, Yain-Whar; Wong, Raymond K.

doi:10.1016/j.ins.2020.12.069

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…• We focused on static graphs whose structure does not change over time. Dealing with dynamic graphs, such as those originated by streaming data sources, would open the way to new interesting applications (see, e.g., [22]). This would require to re-think both the layout step, which should produce layouts that remain stable over time, as well as the rendering step, which should highlight the changes that occur over time.…”

Section: Discussionmentioning

confidence: 99%

“…We point the reader to the extensive surveys of Cheong and Si [8] and by Kobourov [10] on the vast literature on force-directed algorithms, as well as to the surveys by Hu and Shi [9] and by Landesberger et al [21] that are focused on the visualization of large graphs. We also remark that the versatility of force-directed algorithms makes them suitable to visualize dynamic graphs [22], as well as clustered and compound graphs [23], [24]. Common to all force-directed algorithms are a model of the system of forces acting on the vertices and an iterative algorithm to find a static equilibrium of this system, which represents the final layout of the graph.…”

Section: A Force-directed Layout Algorithmsmentioning

confidence: 99%

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BrowVis: Visualizing Large Graphs in the Browser

et al. 2022

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A recent stream of research focuses on building high-performance data analysis and management systems that run completely in the browser. Indeed, today personal devices offer non-trivial amount of computing power, while the latest Web browsers provide powerful JavaScript engines. On the other hand, the use of visualization to present and analyze networks is taking a leading role in conveying information and knowledge to users that operate in multiple domains. In this scenario, the aim of our research is to explore the scalability limits of a system that executes the full graph visualization pipeline entirely in the browser. In this paper, we present BrowVis, a self-contained system to compute interactive visualizations of large graphs in the browser. Experiments show that, on a common laptop, BrowVis can visualize graphs with thousands of elements in seconds, as well as graphs with millions of elements in minutes. Once the initial visualization has been computed, BrowVis makes it possible to interactively explore the represented graph by following a details-on-demand paradigm. The use of BrowVis in practice is demonstrated by a case study dealing with a real-world scientific collaboration network.

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Section: Discussionmentioning

confidence: 99%

Section: A Force-directed Layout Algorithmsmentioning

confidence: 99%

BrowVis: Visualizing Large Graphs in the Browser

et al. 2022

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“…Visualization Force-directed graph [22][23][24][25] is used to visualize the scholar network as it provides the ability to convey the relationship between data, the weightage of the relationship, and the flow often brings out the untold insights into the limelight.…”

Section: B Fruchterman-reingold Algorithm For Vertices and Edgesmentioning

confidence: 99%

Dynamic Force-directed Graph with Weighted Nodes for Scholar Network Visualization

Aris¹,

Ramasamy²,

Aris³

et al. 2022

IJACSA

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Through the growth of portals and venues to publish academic publications, the number of academic publications is growing exponentially in recent years. An effective exploration and fast navigation in the collection of academic publications become an urgent need to help academic researchers find publications related to their research and the surrounding community. A scholar network visualization approach is proposed to help users to explore a large number of academic publications concerning the strength of the relationship between each publication. The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation. The proposed approach visualizes data by incorporating a force-directed graph with weighted nodes and vertices to give more descriptive information of millions of raw data such as author names, publication title, publication year, publication venue and number of citations from the scholar network dataset. By introducing a weighted relationship in the network visualization, the proposed approach can give a more insightful detail of each publication such as a highly cited publication by looking at and exploring the generated interactive graph. The proposal is targeted to be incorporated into a larger-scale scholar network analytical dashboard that can offer various visualization approaches under one flagship application.

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“…On the other hand, prefractal graphs are also dynamic graphs [26][27][28]. Large prefractal graphs are used to build graph models with the help of which optimization problems are solved [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Introduction to the Class of Prefractal Graphs

Kochkarov

2022

Mathematics

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Fractals are already firmly rooted in modern science. Research continues on the fractal properties of objects in physics, chemistry, biology and many other scientific fields. Fractal graphs as a discrete representation are used to model and describe the structure of various objects and processes, both natural and artificial. The paper proposes an introduction to prefractal graphs. The main definitions and notation are proposed—the concept of a seed, the operations of processing a seed, the procedure for generating a prefractal graph. Canonical (typical) and non-canonical (special) types of prefractal graphs are considered separately. Important characteristics are proposed and described—the preservation of adjacency of edges for different ranks in the trajectory. The definition of subgraph-seeds of different ranks is given separately. Rules for weighting a prefractal graph by natural numbers and intervals are proposed. Separately, the definition of a fractal graph as infinite is given, and the differences between the concepts of fractal and prefractal graphs are described. At the end of the work, already published works of the authors are proposed, indicating the main backlogs, as well as a list of directions for new research. This work is the beginning of a cycle of works on the study of the properties and characteristics of fractal and prefractal graphs.

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Online force-directed algorithms for visualization of dynamic graphs

Cited by 8 publications

References 18 publications

BrowVis: Visualizing Large Graphs in the Browser

BrowVis: Visualizing Large Graphs in the Browser

Dynamic Force-directed Graph with Weighted Nodes for Scholar Network Visualization

Introduction to the Class of Prefractal Graphs

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