Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks
September 13, 2023 ยท Declared Dead ยท ๐ IEEE International Conference on Acoustics, Speech, and Signal Processing
"No code URL or promise found in abstract"
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Authors
James S. Nagai, Ivan G. Costa, Michael T. Schaub
arXiv ID
2309.07030
Category
cs.LG: Machine Learning
Cross-listed
cs.SI,
eess.SY,
q-bio.GN,
q-bio.MN
Citations
1
Venue
IEEE International Conference on Acoustics, Speech, and Signal Processing
Last Checked
4 months ago
Abstract
Comparing graphs by means of optimal transport has recently gained significant attention, as the distances induced by optimal transport provide both a principled metric between graphs as well as an interpretable description of the associated changes between graphs in terms of a transport plan. As the lack of symmetry introduces challenges in the typically considered formulations, optimal transport distances for graphs have mostly been developed for undirected graphs. Here, we propose two distance measures to compare directed graphs based on variants of optimal transport: (i) an earth movers distance (Wasserstein) and (ii) a Gromov-Wasserstein (GW) distance. We evaluate these two distances and discuss their relative performance for both simulated graph data and real-world directed cell-cell communication graphs, inferred from single-cell RNA-seq data.
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