Blockmap: an interactive visualization tool for big-data networks

被引:3
|
作者
Frantz, Terrill L. [1 ]
机构
[1] Peking Univ, HSBC Business Sch, Management, Shenzhen, Peoples R China
关键词
Network analysis; Large datasets; Data visualization; Treemap; Heatmap; Software;
D O I
10.1007/s10588-017-9252-6
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This article describes the Blockmap, which is a mechanism for displaying and exploring network datasets. The data are presented in a squarified-mosaic form, which is well-suited for visual display on a computer or phone screen. The relational data are dimension-reduced and structured for interactive, hierarchical exploration. The Blockmap applies a combination of treemap and heatmap display schemes specifically to the analysis of large network datasets. The Blockmap offers the analyst a way to explore underlying node-level data, at the full-network level, according to shared characteristics of the constituent nodes. It offers a technique for exploring nodesets-collections of network nodes-which have been classified according to a user-defined set of rules or discriminative algorithms. Typically, nodes can be classified according to their common attributes or a stratification of their ego-level network measures, but means can be extended. Using a Blockmap, an analyst can profile a network according to the meaningful characteristics exhibited by the mosaic; this technique also offers theorists a platform for developing a methodological and analytic framework for characterizing and analyzing network data. Production versions of Blockmap technology are presently hosted in client- and web-based software and is available freely in *ORA-LITE.
引用
收藏
页码:149 / 168
页数:20
相关论文
共 50 条
  • [41] A Data Reconstruction Method for The Big-Data Analysis
    Mito, Masataka
    Murata, Kenya
    Eguchi, Daisuke
    Mori, Yuichiro
    Toyonaga, Masahiko
    [J]. 2018 9TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST), 2018, : 319 - 323
  • [42] Overcoming Resistance to Big Data and Operational Changes Through Interactive Data Visualization
    Phillips-Wren, Gloria
    McKniff, Sueanne
    [J]. BIG DATA, 2020, 8 (06) : 528 - 539
  • [43] Towards a New Architecture for Data Multilevels Interactive Visualization in Big Data Domains
    Kahil, Moustafa Sadek
    Bouramoul, Abdelkrim
    Derdour, Makhlouf
    [J]. 2019 4TH INTERNATIONAL CONFERENCE ON NETWORKING AND ADVANCED SYSTEMS (ICNAS 2019), 2019, : 62 - 68
  • [44] VIM: A Big Data Analytics Tool for Data Visualization and Knowledge Mining
    Arafat, Sk. Shariful Islam
    Hossain, Md Shakil
    Hasan, Md. Mahmudul
    Imam, S. M. Al-Hossain
    Islam, Md. Mofijul
    Saha, Sanjay
    Shatabda, Swakkhar
    Juthi, Tamanna Islam
    [J]. 2017 IEEE INTERNATIONAL WIE CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (IEEE WIECON-ECE 2017), 2017, : 224 - 227
  • [45] Voter Privacy and Big-Data Elections
    Judge, Elizabeth F.
    Pal, Michael
    [J]. OSGOODE HALL LAW JOURNAL, 2021, 58 (01): : 1 - 55
  • [46] Big-Data Science: Infrastructure Impact
    Monga, Inder
    Prabhat
    [J]. PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY, 2018, 84 (02): : 359 - 370
  • [47] Persisting big-data: The NoSQL landscape
    Corbellini, Alejandro
    Mateos, Cristian
    Zunino, Alejandro
    Godoy, Daniela
    Schiaffino, Silvia
    [J]. INFORMATION SYSTEMS, 2017, 63 : 1 - 23
  • [48] Big-Data Clustering with Genetic Algorithm
    Mortezanezhad, Afsaneh
    Daneshifar, Ebrahim
    [J]. 2019 IEEE 5TH CONFERENCE ON KNOWLEDGE BASED ENGINEERING AND INNOVATION (KBEI 2019), 2019, : 702 - 706
  • [49] A Minimax Approach for Classification with Big-data
    Krishnan, R.
    Jagannathan, S.
    Samaranayake, V. A.
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 1437 - 1444