Simple Algorithms for Network Visualization:A Tutorial

The graph drawing and information visualization communities have developed many sophisticated techniques for visualizing network data, often involving complicated algorithms that are difficult for the uninitiated to learn. This article is intended for beginners who are interested in programming their own network visualizations, or for those curious about some of the basic mechanics of graph visualization. Four easy-to-program network layout techniques are discussed, with details given for implementing each one： force-directed node-link diagrams, arc diagrams, adjacency matrices, and circular layouts. A Java applet demonstrating these layouts, with open source code, is available at http：//www.michaelmcguffin.com/research/simpleNetVis/. The end of this article also briefly surveys research topics in graph visualization, pointing readers to references for further reading.

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).

Seismic impedance inversion based on cycle-consistent generative adversarial network

Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.

Complex Network Formation and Analysis of Online Social Media Systems

To discover and identify the influential nodes in any complex network has been an important issue.It is a significant factor in order to control over the network.Through control on a network,any information can be spread and stopped in a short span of time.Both targets can be achieved,since network of information can be extended and as well destroyed.So,information spread and community formation have become one of the most crucial issues in the world of SNA(Social Network Analysis).In this work,the complex network of twitter social network has been formalized and results are analyzed.For this purpose,different network metrics have been utilized.Visualization of the network is provided in its original form and then filter out(different percentages)from the network to eliminate the less impacting nodes and edges for better analysis.This network is analyzed according to different centrality measures,like edge-betweenness,betweenness centrality,closeness centrality and eigenvector centrality.Influential nodes are detected and their impact is observed on the network.The communities are analyzed in terms of network coverage considering theMinimum Spanning Tree,shortest path distribution and network diameter.It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook,Instagram,Twitter,LinkedIn,etc.

CONTROL SCHEMES FOR CMAC NEURAL NETWORK-BASED VISUAL SERVOING

In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.

Specific-Scene Oriented Pedestrian Detection in Visual Sensor Network

Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.

NetV.js:A web-based library for high-efficiency visualization of large-scale graphs and networks

Graph visualization plays an important role in several fields,such as social media networks,protein-protein interaction networks,and traffic networks.A number of visualization design tools and programming toolkits have been widely used in graph-related applications.However,a key challenge remains in the high-efficiency visualization of large-scale graph data.In this study,we present NetV.js,an open-source and WebGL-based JavaScript library that supports the fast visualization of large-scale graph data(up to 50 thousand nodes and 1 million edges)at an interactive frame rate with a commodity computer.Experimental results demonstrate that our library outperforms existing toolkits(Sigma.js,D3.js,Cytoscape.js,and Stardust.js)in terms of performance.

Environmental degradation and poverty:A bibliometric review

Understanding the mutual logic between environment and poverty mitigation is vital for achieving the United Nations Sustainable Development Goals(SDGs).This study conducts a bibliometric review of the available literature on environmental degradation and poverty and summarizes the existing researches.By applying suitable keywords,we retrieved 175 peer-reviewed articles from the Web of Science published between 1993 and 2020.We utilized the visualization of similarity viewer(VOSviewer)for this bibliometric study and classified the leading publications,prominent journals,and institutions.Furthermore,our bibliometric review found a phenomenon in investigation that people are indifference about the impact of environmental degradation on rising poverty levels in poor and developing countries.In terms of contributions,this study classifies 4 leading thematic clusters that identify how environmental degradation increases poverty.By employing text mining analysis,this research connects specific environmental terms accountable for the recent rise in global poverty.We finally recommend that including other databases to strengthen the findings of environmental degradation and poverty is one of the future research directions.

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.

Insights into financial technology (FinTech):a bibliometric and visual study

This paper conducted a comprehensive analysis based on bibliometrics and science mapping analysis.First,848 publications were obtained from Web of Science.Their fundamental characteristics were analyzed,including the types,annual publications,hot research directions,and foci(by theme analysis,co-occurrence analysis,and timeline analysis of author keywords).Next,the prolific objects(at the level of countries/regions,institutions,journals,and authors)and corresponding pivotal cooperative relationship networks were used to highlight who pays attention to FinTech.Furthermore,the citation structures of authors and journals were investigated,including citation and co-citation.Additionally,this paper presents the burst detection analysis of cited authors,journals,and references.Finally,combining the analysis results with the current financial environment,the challenges and future development opportunities are discussed further.Accordingly,a comprehensive study of the FinTech documents not only reviews the current research characteristics and trajectories but also helps scholars find the appropriate research entry point and conduct in-depth research.

Visualization of Biomolecular Networks' Comparison on Cytoscape

Similarities and dissimilarities between biomolecular networks cannot be intuitively recognized even after the development of several comparison algorithms because of the lack of visualization tools. In this paper, an integrated tool kit named Biomolecular Network Match（BNMatch） is designed and developed based on Cytoscape—a popular and open-source tool for analyzing and visualizing networks. BNMatch integrates the comparison of the outputs of algorithms used for processing biomolecular networks and expresses the matching data between them by defining similar vertices and links with similar attributes. Moreover, in order to maintain consistency, their counterparts in other networks change when the nodes and edges in one of the compared networks are changed. It becomes easy for users to analyze similar networks by invoking comparison algorithms and visualizing the matching data between the networks using BNMatch.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

SEIR-SW, Simulation Model of Influenza Spread Based on the Small World Network

This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR（Susceptible-Exposed-Infected-Removed）, through extensive simulations of the effects of social network on epidemic spread in a Small World（SW） network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread,allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.

ETCM v2.0:An update with comprehensive resource and rich annotations for traditional Chinese medicine

Existing traditional Chinese medicine(TCM)-related databases are still insufficient in data standardization,integrity and precision,and need to be updated urgently.Herein,an Encyclopedia of Traditional Chinese Medicine version 2.0(ETCM v2.0,http://www.tcmip.cn/ETCM2/front/#/)was constructed as the latest curated database hosting 48,442 TCM formulas recorded by ancient Chinese medical books,9872 Chinese patent drugs,2079 Chinese medicinal materials and 38,298 ingredients.To facilitate the mechanistic research and new drug discovery,we improved the target identification method based on a two-dimensional ligand similarity search module,which provides the confirmed and/or potential targets of each ingredient,as well as their binding activities.Importantly,five TCM formulas/Chinese patent drugs/herbs/ingredients with the highest Jaccard similarity scores to the submitted drugs are offered in ETCM v2.0,which may be of significance to identify prescriptions/herbs/ingredients with similar clinical efficacy,to summarize the rules of prescription use,and to find alternative drugs for endangered Chinese medicinal materials.Moreover,ETCM v2.0 provides an enhanced Java Script-based network visualization tool for creating,modifying and exploring multi-scale biological networks.ETCM v2.0 may be a major data warehouse for the quality marker identification of TCMs,the TCM-derived drug discovery and repurposing,and the pharmacological mechanism investigation of TCMs against various human diseases.

Visualizing ordered bivariate data on node-link diagrams

Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.

A Comprehensive Overview of the Role of Visual Cortex Malfunction in Depressive Disorders: Opportunities and Challenges

Major depressive disorder(MDD)is a highly heterogeneous mental disorder,and its complex etiology and unclear mechanism are great obstacles to the diagnosis and treatment of the disease.Studies have shown that abnormal functions of the visual cortex have been reported in MDD patients,and the actions of several antidepressants coincide with improvements in the structure and synaptic functions of the visual cortex.In this review,we critically evaluate current evidence showing the involvement of the malfunctioning visual cortex in the pathophysiology and therapeutic process of depression.In addition,we discuss the molecular mechanisms of visual cortex dysfunction that may underlie the pathogenesis of MDD.Although the precise roles of visual cortex abnormalities in MDD remain uncertain,this undervalued brain region may become a novel area for the treatment of depressed patients.

Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring sites.However,there is still great challenge because of the limited wireless network bandwidth.To resolve the problem,we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image frames.Firstly,an adaptively learning background model is improved to discover successive similar image frames from the inputting video sequence.Then,the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element dynamics.Furthermore,a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or not.To evaluate the trade-off performance of the proposed method,it is tested on the CDW-2014 dataset,which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes,and the misdetection rate of the undetected foreground frames is near to zero.Compared to the original stream,it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

传统数据中心网络向AD-DC平滑演进技术分析

本文阐述了传统数据中心网络如何向AD-DC平滑演进的三种场景,每种场景对应的迁移方案,以及通过一个示例描述整个迁移过程。随着云计算、大数据、移动互联网的兴起,数据中心流量与日俱增,业务上线节奏加快,要求数据中心网络做出快速响应,传统数据中心网络向AD-DC演进将是必然趋势,AD-DC解决方案满足数字化转型中不断扩张的网络需求,不断迭代创新为客户提供智能融合、安全可靠、先进成熟、开放包容的全栈数据中心网络解决方案,助力更多行业客户的数字化转型实践。