Journal
IEEE ACCESS
Volume 6, Issue -, Pages 53006-53018Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2870684
Keywords
Dynamic network visualization; massive sequence view; graph sampling; visual abstraction
Categories
Funding
- National Key Research and Development Program of China [2018YFB0904503]
- National Science Foundation of China [61672538, 61772456, 61872388, 61872389]
- Natural Science Foundation of Hunan Province [2017JJ3414]
- Open Project Program of the State Key Lab of CAD&CG, Zhejiang University [A1812]
- Open Research Fund of the Key Laboratory of Network Assessment Technology, Institute of Information Engineering, Chinese Academy of Sciences
Ask authors/readers for more resources
Dynamic network visualization is crucial to understand network evolving behavior. Massive sequence view (MSV) is a classic technique for visualizing dynamic networks and provides users with a fine-grained presentation of time-varying communication trend from both node pair and global network levels. However, MSV is vulnerable to visual clutter caused by overlapping edges, failing to show clear patterns or trends. This paper presents an edge sampling method, using the edge overlapping degree (EOD) concept, to reduce visual clutter in MSV while preserving the time-varying features of network communication. Referring to accept-reject sampling, we use kernel density estimation to characterize the time-varying features between node pairs and generate EOD probability density functions to accomplish sampling in a bottom-up manner. To enhance the sampling effect, we also consider the edge length factor and streaming processing. The case studies on two dynamic network data sets demonstrate that our method can significantly improve the overall readability of MSV and clearly reveal the temporal features of both node pairs and global network. A quantitative evaluation comparing with two other sampling methods using three real-world data sets indicates that our method can well balance visual clutter reduction and temporal feature preservation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available