skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Zeng, Jianping"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ; ( , The 50th International Symposium on Computer Architecture (ISCA))
    Free, publicly-accessible full text available June 1, 2024
  2. ; ; ( , Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing)
    Full Text Available 
  3. ; ; ; ( , 54th Annual IEEE/ACM International Symposium on Microarchitecture)
    Full Text Available 
  4. ; ; ; ; ; ; ( , 54th Annual IEEE/ACM International Symposium on Microarchitecture)
    Full Text Available 
  5. ; ; ; ; ; ( , 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI ’20))
    null (Ed.)
    Full Text Available 
  6. ; ( , 2019 IEEE International Conference on Big Data (Big Data))
    Full Text Available 
  7. ; ( , 2018 IEEE International Conference on Cluster Computing (CLUSTER))
    Full Text Available 
  8. ; ( , Proceedings of the 47th International Conference on Parallel Processing)
    Full Text Available 
  9. ; ; ; ( , Graph Drawing and Network Visualization: 25th International Symposium, GD 2017)
    Edge bundling methods can effectively alleviate visual clutter and reveal high-level graph structures in large graph visualization. Researchers have devoted significant efforts to improve edge bundling according to different metrics. As the edge bundling family evolve rapidly, the quality of edge bundles receives increasing attention in the literature accordingly. In this paper, we present MLSEB, a novel method to generate edge bundles based on moving least squares (MLS) approximation. In comparison with previous edge bundling methods, we argue that our MLSEB approach can generate better results based on a quantitative metric of quality, and also ensure scalability and the efficiency for visualizing large graphs. 
    more » « less
    Full Text Available