Filtering Edge for Exploration of Large Graphs

Research output: Book chapter/Published conference paperConference paperpeer-review

1 Citation (Scopus)


Visual clutter in the layout of a large graph is mainly caused by the overwhelming number of edges. Filtering is one of ways to reduce the clutter. We regard a filtered graph as the compressed one of an original graph. Based on this view, a filtering approach is presented to reduce the visual clutter of a layout in a way that hidden patterns can be revealed gradually. The experiments have demonstrated the performance of the proposed approach in our prototype system. As evidenced by real examples, the system allows users to explore a graph at adjustable, continuous levels of details in an interactive way. This new approach is able to reveal more hidden patterns in graphs than existing approaches, providing a new way to gain insights into graph data
Original languageEnglish
Title of host publicationProceedings of the IEEE Symposium on Large Data Analysis and Visualization 2013
EditorsBerk Geveci , Hanspeter Pfister, Venkatram Vishwanath
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages2
ISBN (Electronic)9781479916580
Publication statusPublished - 2013
EventIEEE Visualization (VIS 2013): 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV) - Marriott Marquis Hotel, Atlanta, United States
Duration: 13 Oct 201314 Oct 2013 (Conference website) (Parent conference website) (Conference proceedings)


ConferenceIEEE Visualization (VIS 2013)
Country/TerritoryUnited States
OtherIn many areas of science, simulations and experiments begin to generate many petabytes of data, with some sciences facing exabytes of data near term. Similarly, the collection of information about the Internet applications and users for a variety of purposes is generating only more data. Our ability to manage, mine, analyze, and visualize the data is fundamental to the knowledge discovery process. That is, the value of data at extreme scale can be fully realized only if we have an end-to-end solution, which demands a collective, inter-disciplinary effort to develop.

This new symposium, held in conjunction with IEEE Vis 2013, aims at bringing together domain scientists, data analytics and visualization researchers, and users, and fostering the needed exchange to develop the next-generation data-intensive analysis and visualization technology. Attendees will be introduced to the latest and greatest research innovations in large data management, analysis, and visualization, learn how these innovations impact data intensive computing and knowledge discovery, and also learn about the critical issues in creating a complete solution through both invited and contributed talks, and panel discussion. Paper submissions are solicited for a long paper event that describes large data visualization techniques and systems, and a short paper event for practitioners to describe and present their large data visualization applications. Topic emphasis is on algorithms, languages, systems and hardware that supports the analysis and visualization of large data.
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