Next Article in Journal / Special Issue
Constructing Interactive Visual Classification, Clustering and Dimension Reduction Models for n-D Data
Previous Article in Journal / Special Issue
Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure
Article

PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Achim Ebert and Gunther H. Weber
Informatics 2017, 4(3), 22; https://doi.org/10.3390/informatics4030022
Received: 19 June 2017 / Revised: 11 July 2017 / Accepted: 12 July 2017 / Published: 18 July 2017
(This article belongs to the Special Issue Scalable Interactive Visualization)
Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains explore large graphs interactively and efficiently to find out what is ‘important’? How can multiple researchers explore a new graph dataset collectively and “help” each other with their findings? In this article, we present Perseus-Hub, a large-scale graph mining tool that computes a set of graph properties in a distributed manner, performs ensemble, multi-view anomaly detection to highlight regions that are worth investigating, and provides users with uncluttered visualization and easy interaction with complex graph statistics. Perseus-Hub uses a Spark cluster to calculate various statistics of large-scale graphs efficiently, and aggregates the results in a summary on the master node to support interactive user exploration. In Perseus-Hub, the visualized distributions of graph statistics provide preliminary analysis to understand a graph. To perform a deeper analysis, users with little prior knowledge can leverage patterns (e.g., spikes in the power-law degree distribution) marked by other users or experts. Moreover, Perseus-Hub guides users to regions of interest by highlighting anomalous nodes and helps users establish a more comprehensive understanding about the graph at hand. We demonstrate our system through the case study on real, large-scale networks. View Full-Text
Keywords: large-scale graphs; visualization; visual analytics; distributions; interaction; distributed computations; anomaly detection; collective analysis large-scale graphs; visualization; visual analytics; distributions; interaction; distributed computations; anomaly detection; collective analysis
Show Figures

Figure 1

MDPI and ACS Style

Jin, D.; Leventidis, A.; Shen, H.; Zhang, R.; Wu, J.; Koutra, D. PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs. Informatics 2017, 4, 22. https://doi.org/10.3390/informatics4030022

AMA Style

Jin D, Leventidis A, Shen H, Zhang R, Wu J, Koutra D. PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs. Informatics. 2017; 4(3):22. https://doi.org/10.3390/informatics4030022

Chicago/Turabian Style

Jin, Di, Aristotelis Leventidis, Haoming Shen, Ruowang Zhang, Junyue Wu, and Danai Koutra. 2017. "PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs" Informatics 4, no. 3: 22. https://doi.org/10.3390/informatics4030022

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop