We present FLOWGEN, a graph-generation model inspired by the dual-process theory of mind that generates large graphs incrementally. Depending on the difficulty of completing the graph at the current step, graph generation is routed to either a fast~(weaker) or a slow~(stronger) model. fast and slow models have identical architectures, but vary in the number of parameters and consequently the strength. Experiments on real-world graphs show that ours can successfully generate graphs similar to those generated by a single large model in a fraction of time.
翻译:我们展示了FLOWGEN, 这是一种受双进程思维理论启发的图形生成模型, 逐渐生成大型图形。 取决于在当前步骤完成图形的难度, 图形生成可选择快速的 ~ (weker) 或慢的~ (stronger) 模型。 快速和慢的模型具有相同的结构, 但参数数量和强度各不相同 。 现实世界图形上的实验显示, 我们的图形能够成功生成与单个大模型在很短的时间内生成的图形相似的图形 。