Graph Analysis, Inference, and Visualization

The graph is a mathematical structure used to model networks (e.g. social networks, transportation networks) in many different applications. Since the graph is a unique non-Euclidean data structure, modeling graph data remained a challenging task until Graph Neural Networks (GNNs) emerged. Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. GNNs for graph visualization is an important topic but is still under-explored. In the GRAVITY lab, we aim to visualize graphs with diverse aesthetic goals via GNNs, such that the topological characteristic of graphs can be clearly identified. In addition to applying GNNs in the visualization field, we also focus on visualizing and explaining the decision-making process of GNNs because GNNs' lack of self-explainability becomes a serious obstacle for applying GNNs to real-world problems. In summary, our ultimate goal is not only to visualize the graphs with the most advanced deep learning technique, but also to open the black-box (i.e., graph-based deep learning model) by disclosing its decision-making process.

Publications:

• Xiaoqi Wang and Han-Wei Shen. "GNNBoundary: Towards Explaining Graph Neural Networks Through the Lens of Decision Boundaries." In International Conference on Learning Representations, 2024.

• Xiaoqi Wang, Kevin Yen, Yifan Hu, Han-Wei Shen. "SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals." IEEE Transactions on Visualization and Computer Graphics, 2023.

• Xiaoqi Wang and Han-Wei Shen. "GNNInterpreter: A Probabilistic Generative Model-Level Explanation For Graph Neural Networks." In International Conference on Learning Representations, 2023.

• Xiaoqi Wang, Kevin Yen, Yifan Hu, Han-Wei Shen: DeepGD: A Deep Learning Framework for Graph Drawing Using GNN, IEEE Computer Graphics and Applications (2021)