CAREER: New Frontiers in Graph Generation

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A graph is a model of many different types of connections, relationships, or networks between different entities. It can be used to represent micro-level objects, like molecules being recorded as bonds connecting atoms, and macro-level networks, like social networks consisting of connections between users. Graph structures often contain rich information and are large in scale. An important task is to synthesize new graphs that are similar to but different from existing ones. For example, a drug design task may require a model to generate new molecule graphs for screening; and a data-sharing task for a social network may need a model to synthesize and share a graph similar to the original network, without releasing sensitive link information. This project combines neural networks and probabilistic methods to develop tools for generating new graphs for a wide range of tasks. These tools also have a solid statistical foundation and help to deepen the understanding of graph data.This project will advocate a new direction of developing graph generative models based on discrete sequential processes—the generative model starts from a random or trivial graph and tailors it in multiple steps to generate a random graph. The research effort in this project has three technical aims. First, the project will develop a probabilistic framework for building graph generative models with neural networks. Second, the project will overcome efficiency issues in model training and model predictions. Third, the project will compare newly developed models with traditional random graph models to deepen the understanding of network data. From the comparison, new methods will also be developed to preserve private information in the sharing of network data. Models to be developed from this project will have a solid statistical foundation and be connected to traditional random graph models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.