Interpreting Graph Drawing with Multi-Agent Reinforcement Learning
This work provides a novel framework for graph visualization researchers, though it is incremental in unifying existing algorithms rather than introducing a breakthrough method.
The paper tackles the problem of graph drawing by interpreting it as a multi-agent reinforcement learning (MARL) problem, showing that classic algorithms like force-directed layouts can be unified under this framework and enabling the creation of novel algorithms through diverse reward functions, with results producing aesthetically pleasing layouts comparable to classic methods.
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.