论文标题
用多代理增强学习解释图形
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning
论文作者
论文摘要
将机器学习技术应用于图形图已成为可视化研究的新兴领域。在本文中，我们将图形解释为多代理增强学习（MARL）问题。我们首先证明，可以在MARL的框架内解释大量的经典图形绘制算法，包括实力定向的布局和应力大量化。使用此解释，图中的一个节点被分配给具有奖励函数的代理。通过多代理奖励最大化，我们获得了与经典算法的输出相当的美学图表布局。图形图形绘图的MARL框架的主要优势在于，它不仅统一了一般公式中的许多经典图形算法，而且还通过引入各种奖励函数来支持新颖的图形绘图算法的创建。
Applying machine learning techniques to graph drawing has become an emergent area of research in visualization. In this paper, we interpret graph drawing as a multi-agent reinforcement learning (MARL) problem. We first demonstrate that a large number of classic graph drawing algorithms, including force-directed layouts and stress majorization, can be interpreted within the framework of MARL. Using this interpretation, a node in the graph is assigned to an agent with a reward function. Via multi-agent reward maximization, we obtain an aesthetically pleasing graph layout that is comparable to the outputs of classic algorithms. The main strength of a MARL framework for graph drawing is that it not only unifies a number of classic drawing algorithms in a general formulation but also supports the creation of novel graph drawing algorithms by introducing a diverse set of reward functions.
