SHADOWCAST: Controllable Graph Generation
This addresses the need for practitioners to generate and understand graphs with specific structures, though it appears incremental as it builds on existing generative adversarial networks.
The paper tackles the problem of controllable graph generation by introducing a method to produce graphs with desired attributes while preserving intrinsic properties, demonstrating competitive performance on three real-world datasets.
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.