ivga: A fast force-directed method for interactive visualization of complex networks

Dzwinel, Witold; Wcisło, Rafał; Czech, Wojciech

doi:10.1016/j.jocs.2016.09.001

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…In the visualization result of [21], several obvious central nodes divide the hosts in the network into different areas, in which is more likely to form centers due to unusual network scanning behaviors. Dzwinel et al [22] proposed a new and fast graph-drawing method called ivga to support visual analysis for complex networks consisting of |V| 106+ vertices. Böhringer and Paulisch [23] added user constraints when generating the final layout, which can generate a more aesthetic rendering result and increase the usefulness of the current layout.…”

Section: Network Connection Visualizationmentioning

confidence: 99%

Owleyes: A Visual Analytics System for Functions and Connection Patterns of IPv4 Addresses in Networks

Yan

He²,

Liu

et al. 2020

IEEE Access

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Netflow log files commonly contain massive transfer records in tiny time interval, making analytical works complex and burdensome. By combining human cognition abilities with computerized techniques, visual analytics systems have become efficient tools for showing network states and locating abnormal behaviors. However, traditional visual analytics systems tend to be designed for solving certain problems and unable to synthesize various types of data sources. Despite recent advances in network security visualization, academia still starves for a proper solution to visualize IPv4 address behavior modes and IPv4 connection patterns within limited drawing space. Thus, we propose a visual analytics system called 'Owleyes' which reprocesses Netflow log data with simple statistical operations in basic dimensions and fulfills the aforementioned requirements with proper novel graphs such as 'sunburst-hive-plot graph' (SHG) and link-wheel graph (LW). The SHG provides a stable and comparable means of visualizing connection patterns efficiently in a limited drawing space. The LW represents the hourly connection counts of main ports in a specific IPv4 connection during one day. With the use case dealing with the ChinaVis 2016 Challenge I data, the efficiency and practicability of Owleyes are demonstrated. INDEX TERMS Visual analytics, network security, sunburst-hiveplot graph, link wheel graph, user-centric interaction. HAIBO HU received the master's degree in software engineering and the Ph.D. degree in computer science from Chongqing University, China, in 2004 and 2012, respectively. He is currently an Associate Professor with the School of Big Data and Software Engineering, Chongqing University. He is also a Research Associate with the Key Laboratory for Dependable Service Computing in Cyber Physical Society of the Ministry of Education, China. His current research interests include software engineering, pattern recognition, semantic web, and visual analytics.

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Section: Network Connection Visualizationmentioning

confidence: 99%

Owleyes: A Visual Analytics System for Functions and Connection Patterns of IPv4 Addresses in Networks

Yan

He²,

Liu

et al. 2020

IEEE Access

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“…20,54–61 Multidimensional scaling is also useful for energy function minimisation modelling, as it can improve the layout of networks with high-degree nodes. Dwyer et al 62 and Dzwinel et al 63 proposed a multidimensional scaling KK algorithm 19 with the use of stress majorisation optimisation. The energy function proposed by Dzwinel et al 63 is defined as follows…”

Section: Force-directed Algorithmsmentioning

confidence: 99%

“…Dwyer et al 62 and Dzwinel et al 63 proposed a multidimensional scaling KK algorithm 19 with the use of stress majorisation optimisation. The energy function proposed by Dzwinel et al 63 is defined as follows…”

Section: Force-directed Algorithmsmentioning

confidence: 99%

Force-directed algorithms for schematic drawings and placement: A survey

Cheong

2019

Information Visualization

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Force-directed algorithms have been developed over the last 50 years and used in many application fields, including information visualisation, biological network visualisation, sensor networks, routing algorithms, scheduling, and graph drawing. Our survey provides a comprehensive summary of developments and a full roadmap for state-of-the-art force-directed algorithms in schematic drawings and placement. We classified the model of force-directed algorithms into classical and hybrid. The classical force-directed algorithms are further classified as follows: (a) accumulated force models, (b) energy function minimisation models and (c) combinatorial optimisation models. The hybrid force-directed algorithms are classified as follows: (a) parallel and hardware accelerated models, (b) multilevel force-directed models and (c) multidimensional scaling force-directed algorithms. Five categories of application domains in which force-directed algorithms have been adopted for schematic drawings and placement are also summarised: (a) aesthetic drawings for general networks, (b) component placement and scheduling in high-level synthesis of very-large-scale integration circuits design, (c) information visualisation, (d) biological network visualisation and (e) node placement and localisation for sensor networks.

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“…Then, one can assume, that only the distances of each sample y i to their nn nearest neighbors are Euclidean. As shown in [29,9], the nn nearest neighbor graph (nn-graph), in which vertices represent data samples while edges the connections to their nn nearest neighbors, can be treated as an approximation of this low-dimensional manifold, immersed in a high-dimensional feature space (see Figure 2). In CDA and Isomap [34,29] DE algorithms, the proximities of more distant graph vertices (samples) are calculated as the lengths of the shortest paths between them in a respective nn-graph.…”

Section: Key Conceptmentioning

confidence: 99%

“…The particles evolve in 2 space with discrete time n scaled by the timestep ∆t, according to the Newtonian equations of motion discretized by using the leapfrog numerical scheme. Here we use their simplified form [9]:…”

Section: Introductionmentioning

confidence: 99%

2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements

Dzwinel,

Wcislo,

Matwin

2019

Preprint

Self Cite

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In the advent of big data era, interactive visualization of large data sets consisting of M ∼ 10 5+ highdimensional feature vectors of length N (N ∼ 10 3+ ), is an indispensable tool for data exploratory analysis. The state-of-the-art data embedding (DE) methods of N-D data into 2-D (3-D) visually perceptible space (e.g., based on t-SNE concept) are too demanding computationally to be efficiently employed for interactive data analytics of large and high-dimensional datasets. Herein we present a simple method, ivhd (interactive visualization of high-dimensional data tool), which radically outperforms the modern data-embedding algorithms in both computational and memory loads, while retaining high quality of N-D data embedding in 2-D (3-D). We show that DE problem is equivalent to the nearest neighbor nn-graph visualization, where only indices of a few nearest neighbors of each data sample has to be known, and binary distance between data samples -0 to the nearest and 1 to the other samples -is defined. These improvements reduce the time-complexity and memory load from O(M log M) to O(M), and ensure minimal O(M) proportionality coefficient as well. We demonstrate high efficiency, quality and robustness of ivhd on popular benchmark datasets such as MNIST, 20NG, NORB and RCV1.

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ivga: A fast force-directed method for interactive visualization of complex networks

Cited by 12 publications

References 18 publications

Owleyes: A Visual Analytics System for Functions and Connection Patterns of IPv4 Addresses in Networks

Owleyes: A Visual Analytics System for Functions and Connection Patterns of IPv4 Addresses in Networks

Force-directed algorithms for schematic drawings and placement: A survey

2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements

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