In online dynamic graph drawing, constraints over nodes and node pairs help preserve a coherent mental map in a sequence of graphs. Defining the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph layout. Most existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting constraints. To solve this problem, we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change, according to which different constraints can be set. These constraints, along with stress function, generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent graphs. Experiments demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.

在在线动态图形绘制中，对节点和节点对的约束有助于在一系列图形中保持连贯的思维导图。由于保留心理地图和满足图形布局的视觉美感的要求，定义约束具有挑战性。大多数现有算法基本上依赖于局部变化，但在设置约束时未能对全局传播进行适当的评估。为了解决这个问题，我们引入了从 PageRank 派生的启发式模型，该模型将节点移动模拟为逆马尔可夫过程，从而对布局变化进行全局分析，根据该模型可以设置不同的约束。这些约束与应力函数一起生成了保持相邻图形之间相对稳定的子结构的空间位置和形状的布局。