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    The visualization technology is an important means of effective data explaining.In recent years,the increasing data on Internet present new challenges to this technology.Firstly,the development of visualization algorithms is summarized from four catalogs,including planar graph,hierarchy graph,directed graph and space graph.Then with complex network as the applied background,the application developments of visualization technology in topology complex network,propagation mechanism and intrusion detection are introduced,which show the promoting role of visualization technology in the research of complex network.Finally,several visualization tools suitable for complex network are analyzed and compared.
    Citations (1)
    Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.
    Clustering coefficient
    Biological data
    Citations (219)
    The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing.
    Biological data
    Citations (222)
    Abstract Summary: Network-level visualization of functional data is a key aspect of both analysis and understanding of biological systems. In a continuing effort to create clear and integrated visualizations that facilitate the gathering of novel biological insights despite the overwhelming complexity of data, we present here the GrAph LANdscape VisualizaTion (GALANT), a Cytoscape plugin that builds functional landscapes onto biological networks. By using GALANT, it is possible to project any type of numerical data onto a network to create a smoothed data map resembling the network layout. As a Cytoscape plugin, GALANT is further improved by the functionalities of Cytoscape, the popular bioinformatics package for biological network visualization and data integration. Availability: http://www.lbbc.ibb.unesp.br/galant. Contact: esther@ibb.unesp.br Supplementary Information: Supplementary data are available at Bioinformatics online.
    Plug-in
    Biological data
    Network Analysis
    Multi-layer network visualization techniques have been developed so that users can firstly overview the largescale network and then explore the interesting parts of the data. Meanwhile, local features of the networks are often more interesting rather than their overall structures. It often happens with particular kinds of applications such as social networks. We developed a visualization technique for such types of large-scale networks. The technique iteratively applies a graph sampling algorithm to extract small-scale sub-networks from a large-scale network and then visualize the features of the sub-networks as hierarchically arranged icons. User-specified sub-networks are then visualized by applying our own graph visualization technique. Using networks generated from Twitter data, we actually visualize small-scale networks using the proposed method.
    Biological processes such as metabolic pathways, gene regulation or protein-protein interactions are often represented as graphs in systems biology. The understanding of such networks, their analysis, and their visualization are today important challenges in life sciences. While a great variety of visualization tools that try to address most of these challenges already exists, only few of them succeed to bridge the gap between visualization and network analysis.Medusa is a powerful tool for visualization and clustering analysis of large-scale biological networks. It is highly interactive and it supports weighted and unweighted multi-edged directed and undirected graphs. It combines a variety of layouts and clustering methods for comprehensive views and advanced data analysis. Its main purpose is to integrate visualization and analysis of heterogeneous data from different sources into a single network.Medusa provides a concise visual tool, which is helpful for network analysis and interpretation. Medusa is offered both as a standalone application and as an applet written in Java. It can be found at: https://sites.google.com/site/medusa3visualization.
    Biological data
    Bridge (graph theory)
    Network Analysis
    Graph Layout
    Citations (55)
    Graph visualization techniques can help people to demonstrate and explore the internal relations of the data.With the data size becoming larger and larger,traditional graph visualization techniques are not suitable for massive data visualization.This paper,firstly,summarized traditional graph visualization techniques,then expounded the basic principles and characteristics of visual layout algorithms for massive graph data,and analyzed some visualization software tools.Finally,this paper put forward some suggestions for future research work.
    Graph Layout
    Software visualization
    Citations (1)