Statistical Analysis of Spatial Graphs
- Anna Calissano
Abstract
A spatial graph is a specific type of graph with spatial attributes associated with the nodes and the edges. It is a smart modelling choice for capturing the skeleton of a shape, a blood vessel network, a porous tissue, and many other data objects with intrinsically complex geometry. In this talk, we describe how spatial graphs can be analysed using a specific metric (the Fused Gromov–Wasserstein metric). We extend a testing procedure between distributions of spatial graphs, a depth measure to describe the distribution of spatial graphs, and a dimensionality reduction procedure based on preserving key topological features. We present this variety of methods on a dataset of cardiac fibrosis tissue and on a dataset of fungus mycelium networks.
References:
- Vayer, T., Chapel, L., Flamary, R., Tavenard, R., & Courty, N. (2020). Fused Gromov-Wasserstein Distance for Structured Objects. Algorithms, 13(9), 212.
- Dai, X., Lopez-Pintado, S. (2023). Tukey’s Depth for Object Data. Journal of the American Statistical Association, 118(543), 1760–1772.
- Dubey, P., Chen, Y., & Müller, H. G. (2024). Metric statistics: Exploration and inference for random objects with distance profiles. The Annals of Statistics, 52(2), 757-792.
- Hashemi, M., Gong, S., Ni, J., Fan, W., Prakash, B. A., & Jin, W. (2024). A comprehensive survey on graph reduction: sparsification, coarsening, and condensation. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 8058-8066.
- Calissano, A., Feragen, A., & Vantini, S. (2024). Populations of unlabelled networks: Graph space geometry and generalized geodesic principal components. Biometrika, 111(1), 147-170.