Hierarchical graph visualization using neural networks

An algorithm based on a Hopfield network for solving the hierarchical graph visualization problem is presented. It simultaneously minimizes the number of crossings and total path length to produce two-dimensional drawings easily interpreted by human observers. Traditional heuristics often follow a more local optimization approach where “readability” criteria are sequentially applied, such as applying the barycentric heuristic followed by the priority layout heuristic. As a result of the more global approach, the neural network achieved comparable crossing minimization to the barycentric heuristic while simultaneously reducing total path length up to 50% over the priority layout heuristic for the benchmarks tested