EOD Edge Sampling for Visualizing Dynamic Network via Massive Sequence View

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.