StructMatrix: Large-Scale Visualization of Graphs by Means of Structure Detection and Dense Matrices
StructMatrix: Large-Scale Visualization of Graphs by Means of Structure Detection and Dense Matrices
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DOI：
10.1109/icdmw.2015.205
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
H. Gualdron;R. Cordeiro;J. F. Rodrigues
中科院分区：
文献类型：
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作者：
H. Gualdron;R. Cordeiro;J. F. Rodrigues
给定一个具有数百万个节点和边的大规模图，如何揭示感兴趣的宏观模式，如集团，二分核，星和链？此外，如何将这些模式可视化，从图中获得见解，以支持明智的决策？虽然有许多算法和可视化技术来分析图，但现有的方法都不能大规模地呈现图的结构信息。因此，本文介绍了结构矩阵，一种方法，旨在高可扩展性的视觉检查的图形结构的目标是揭示感兴趣的宏观模式。StructMatrix结合了算法结构检测和邻接矩阵可视化，以呈现给定图中发现的结构的基数，分布和关系特征。我们在真实的大规模图中进行了实验，这些图具有多达一百万个节点和数百万条边。StructMatrix显示，高相关性的图表（例如，Web、Wikipedia和DBLP）都有反映其相应领域性质的特征描述，但我们的研究结果迄今尚未在文献中见到。我们希望我们的技术将为大型图挖掘带来更深入的见解，利用它们来进行决策。
Given a large-scale graph with millions of nodes and edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of the existing approaches is able to present the structural information of graphs at large-scale. Hence, this paper describes StructMatrix, a methodology aimed at high-scalable visual inspection of graph structures with the goal of revealing macro patterns of interest. StructMatrix combines algorithmic structure detection and adjacency matrix visualization to present cardinality, distribution, and relationship features of the structures found in a given graph. We performed experiments in real, large-scale graphs with up to one million nodes and millions of edges. StructMatrix revealed that graphs of high relevance (e.g., Web, Wikipedia and DBLP) have characterizations that reflect the nature of their corresponding domains, our findings have not been seen in the literature so far. We expect that our technique will bring deeper insights into large graph mining, leveraging their use for decision making.