论文标题
影子播：可控的图生成
SHADOWCAST: Controllable Graph Generation
论文作者
论文摘要
我们介绍了可控的图生成问题，该问题在生成过程中以控制图形属性为控制的图形属性，以生成具有可理解结构的所需图形。使用透明而直接的马尔可夫模型来指导这种生成过程，从业者可以塑造和理解生成的图形。我们建议$ {\ rm s {\ small hadow} c {\ small ast}} $，这是一种生成模型，能够控制图生成，同时保留原始图的内在属性。所提出的模型基于条件生成对抗网络。给定观察到的图形和一些用户指定的Markov模型参数，$ {\ rm s {\ small hadow} c {\ small ast}} $控制着生成所需图的条件。在三个现实世界网络数据集上进行的全面实验展示了我们模型在图形生成任务中的竞争性能。此外，我们通过指导$ {\ rm s {\ small hadow} c {\ small ast}} $来显示其有效的可控性，以生成具有不同图形结构的假设场景。
We introduce the controllable graph generation problem, formulated as controlling graph attributes during the generative process to produce desired graphs with understandable structures. Using a transparent and straightforward Markov model to guide this generative process, practitioners can shape and understand the generated graphs. We propose ${\rm S{\small HADOW}C{\small AST}}$, a generative model capable of controlling graph generation while retaining the original graph's intrinsic properties. The proposed model is based on a conditional generative adversarial network. Given an observed graph and some user-specified Markov model parameters, ${\rm S{\small HADOW}C{\small AST}}$ controls the conditions to generate desired graphs. Comprehensive experiments on three real-world network datasets demonstrate our model's competitive performance in the graph generation task. Furthermore, we show its effective controllability by directing ${\rm S{\small HADOW}C{\small AST}}$ to generate hypothetical scenarios with different graph structures.
