GENERIC: Iterative Structural Inference of Directed Graphs

  • 11 March 2023

By Aoran Wang and Prof. Jun Pang

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track.

In this paper, we propose a variational model, iterative Structural Inference of Directed Graphs (iSIDG), to infer the existence of directed interactions from observational agents’ features over a time period in a dynamical system. First, the iterative process in our model feeds the learned interactions back to encourage our model to eliminate indirect interactions and to emphasize directional representation during learning. Second, we show that extra regularization terms in the objective function for smoothness, connectiveness, and sparsity prompt our model to infer a more realistic structure and to further eliminate indirect interactions. We evaluate iSIDG on various datasets including biological networks, simulated fMRI data, and physical simulations to demonstrate that our model is able to precisely infer the existence of interactions, and is significantly superior to baseline models.

If you want to know more about the project, do not hesitate to go to the paragraph dedicated to the project GENERIC in the 2021 Audacity projects.

  • University of Luxembourg

    Prof. Jun Pang

    Faculty of Science, Technology and Medicine
    Assistant professor
    Department of Computer Science
  • University of Luxembourg

    Dr. Lasse Sinkkonen

    Faculty of Science, Technology and Medicine
    Research scientist
    Department of Life Sciences and Medicine