LIC-GAN: Language Information Conditioned Graph Generative GAN Model
This addresses graph synthesis for natural language processing applications, but it is incremental as it builds on existing GAN and language modeling techniques.
The paper tackles the problem of generating graphs from natural language queries by introducing LIC-GAN, a model that combines language modeling and GANs to produce graphs matching descriptions, achieving scores of 0.36 and 0.48 on PropMatch and Closeness metrics.
Deep generative models for Natural Language data offer a new angle on the problem of graph synthesis: by optimizing differentiable models that directly generate graphs, it is possible to side-step expensive search procedures in the discrete and vast space of possible graphs. We introduce LIC-GAN, an implicit, likelihood-free generative model for small graphs that circumvents the need for expensive graph matching procedures. Our method takes as input a natural language query and using a combination of language modelling and Generative Adversarial Networks (GANs) and returns a graph that closely matches the description of the query. We combine our approach with a reward network to further enhance the graph generation with desired properties. Our experiments, show that LIC-GAN does well on metrics such as PropMatch and Closeness getting scores of 0.36 and 0.48. We also show that LIC-GAN performs as good as ChatGPT, with ChatGPT getting scores of 0.40 and 0.42. We also conduct a few experiments to demonstrate the robustness of our method, while also highlighting a few interesting caveats of the model.