Visual abstraction of dynamic network via improved multi-class blue noise sampling

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. Several techniques have been proposed to improve the readability of MSV. Sampling is one of the potential methods to reduce visual clutter in MSV. The existing sampling method EOD-ES for MSV has three main disadvantages: (D1) instability of sample results with the same sampling rate, (D2) imbalance of relative densities between node pairs, and (D3) loss of outliers.