Dynamic graph exploration by interactively linked node-link diagrams and matrix visualizations

The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.

A Survey of Time Series Data Visualization Methods

In the era of big data,the general public is more likely to access big data,but they wouldn’t like to analyze the data.Therefore,the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace.Under this background,a new general visualization method for dynamic time series data emerges as the times require.Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public.This method integrates data visualization into short videos,which is more in line with the way people get information in modern fast-paced lifestyles.The modular approach also facilitates public participation in production.This paper summarizes the dynamic visualization methods of time series data ranking,studies the relevant literature,shows its value and existing problems,and gives corresponding suggestions and future research prospects.

Applications of dynamic visual acuity test in clinical ophthalmology

Dynamic visual acuity test(DVAT)plays a key role in the assessment of vestibular function,the visual function of athletes,as well as various ocular diseases.As the visual pathways conducting dynamic and static signals are different,DVATs may have potential advantages over the traditional visual acuity tests commonly used,such as static visual acuity,contrast sensitivity,and static perimetry.Here,we provide a review of commonly applied DVATs and their several uses in clinical ophthalmology.These data indicate that the DVAT has its unique clinical significance in the evaluation of several ocular disorders.

Dynamic Visual Image Modeling Based on Mobile RobotSelf- Organizing Network in Internetof Things Perception Layer

The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular visual system, the real visual images of the object will be obtained. Then through the mobile self-organizing network, a three-dimensional model is rebuilt by synthesizing the returned images. On this basis, we formalize a novel algorithm for multichannel binocular visual three-dimensional images based on fast three-dimensional modeling. Compared with the method based on single binocular visual system, the new algorithm can improve the Integrity and accuracy of the dynamic three-dimensional object modeling. The simulation results show that the new method can effectively accelerate the modeling speed, improve the similarity and not increase the data size.

Structures and dynamics of scientific knowledge networks:An empirical analysis based on a co-word network

Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.

Recent Progress of Marine Geographic Information System in China:A Review for 2006-2010

In this article,the progress of marine geographic information system(MGIS) in China during 2006?2010 is reviewed with emphases on generic MGIS,advanced MGIS and MGIS-based applications.Generic MGIS can be divided into two categories:data-oriented MGIS and user-oriented MGIS,recent achievements of which by Chinese researchers are summarized respectively.Advanced MGIS mainly involves the establishment of 3D virtual marine environment and 'Digital Ocean'.An overview of the 3D MGIS based simulations in the context of ocean phenomena,ocean engineering and ocean battlefield is also presented.Several sug-gestions for future development of MGIS in China are proposed,and trends of development are addressed.

Simulation of Liquid Argon Flow along a Nanochannel： Effect of Applied Force

Liquid argon flow along a nanochannel is studied using molecular dynamics (MD) simulation in this work.Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is used as the MD simulator.The effects of reduced forces at 0.5,1.0 and 2.0 on argon flow on system energy in the form of system potential energy,pressure and velocity profile are described.Output in the form of three-dimensional visualization of the system at steady-state condition using Visual Molecular Dynamics (VMD) is provided to describe the dynamics of the argon atoms.The equilibrium state is reached after 16000 time steps.The effects on system energy,pressure and velocity profile due to reduced force of 2.0 (F2) are clearly distinguishable from the other two lower forces where sufficiently high net force along the direction of the nanochannel for F2 renders the attractive and repulsive forces between the argon atoms virtually non-existent.A reduced force of 0.5 (F0.5) provides liquid argon flow that approaches Poiseuille (laminar) flow as clearly shown by the n-shaped average velocity profile.The extension of the present MD model to a more practical application affords scientists and engineers a good option for simulation of other nanofluidic dynamics processes.

Visual abstraction of dynamic network via improved multi-class blue noise sampling

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.

Assessing Linear Urban Landscape from dynamic visual perception based on urban morphology

As an essential part of the urban landscape,linear urban landscape(LUL)is the interaction between humans and nature,which is closely associated with daily life and brings multiple characteristics to visual perception.Current studies focus on complex models that describe visual perception using static viewpoints,but lossing the continuous and dynamic features of visual perception.This paper provides a general framework that can quantify dynamic visual perception based on urban morphology and improves accuracy in the descriptions of LUL linear spatial characteristics.Based on Beijing-Hangzhou Grand Canal(Hangzhou urban section),the proposed framework combines the indicators of multiple dimensions to quantify dynamic visual perception and emphasizes the continuity of LUL.To represent the dynamic visual perception and the spatial pattern characteristics of LUL,different evaluation criteria of indicators are set according to landscape scales.To minimize subjectivity and uncertainty caused by subjective cognition and fulfill the landscape pattern under different urban development policies,we set up distinct scenario preference patterns.With appropriate fine-tuning of scenario preference patterns and setting of movement types,the proposed method can be adapted to other LUL projects and aspires to provide a general methodology and scientific guidance for urban planning and landscape management.