SHADOWCAST: Controllable Graph Generation

• URL: http://arxiv.org/abs/2006.03774v4
• Date: Fri, 2 Jul 2021 03:10:04 GMT
• Title: SHADOWCAST: Controllable Graph Generation
• Authors: Wesley Joon-Wie Tann, Ee-Chien Chang, and Bryan Hooi
• Abstract summary: We introduce the controllable graph generation problem, formulated as controlling graph attributes during the generative process to produce desired graphs. Using a transparent and straightforward Markov model to guide this generative process, practitioners can shape and understand the generated graphs. We show its effective controllability by directing $rm Ssmall HADOWCsmall AST$ to generate hypothetical scenarios with different graph structures.
• Score: 28.839854765853953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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