@article{mazeletUnsupervisedLearningOptimal,
  title      = {Unsupervised {{Learning}} for {{Optimal Transport}} Plan Prediction between Unbalanced Graphs},
  author     = {Mazelet, Sonia and Flamary, Rémi and Thirion, Bertrand},
  abstract   = {Optimal transport between graphs, based on Gromov-Wasserstein and other extensions, is a powerful tool for comparing and aligning graph structures. However, solving the associated non-convex optimization problems is computationally expensive, which limits the scalability of these methods to large graphs. In this work, we present Unbalanced Learning of Optimal Transport (ULOT), a deep learning method that predicts optimal transport plans between two graphs. Our method is trained by minimizing the fused unbalanced Gromov-Wasserstein (FUGW) loss. We propose a novel neural architecture with cross-attention that is conditioned on the FUGW tradeoff hyperparameters. We evaluate ULOT on synthetic stochastic block model (SBM) graphs and on real cortical surface data obtained from fMRI. ULOT predicts transport plans with competitive loss up to two orders of magnitude faster than classical solvers. Furthermore, the predicted plan can be used as a warm start for classical solvers to accelerate their convergence. Finally, the predicted transport plan is fully differentiable with respect to the graph inputs and FUGW hyperparameters, enabling the optimization of functionals of the ULOT plan.},
  langid     = {english},
  keywords   = {/unread},
  annotation = {Read\_Status: New\\
                Read\_Status\_Date: 2025-06-11T09:08:09.864Z},
  file       = {/home/louis/snap/zotero-snap/common/Zotero/storage/HPZEYMM9/Mazelet et al. - Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs.pdf}
}

@article{nennaLecture2Entropic,
  title      = {Lecture 2: {{Entropic Optimal Transport}}},
  author     = {Nenna, Luca},
  langid     = {english},
  keywords   = {/unread},
  annotation = {Read\_Status: New\\
                Read\_Status\_Date: 2025-06-11T16:06:28.547Z},
  file       = {/home/louis/snap/zotero-snap/common/Zotero/storage/WGFIISDB/Nenna - Lecture 2 Entropic Optimal Transport.pdf}
}

@article{nennaLecture1Monge,
  title      = {Lecture 1 {{Monge}} and {{Kantorovich}} Problems: From Primal to Dual},
  author     = {Nenna, Luca},
  langid     = {english},
  keywords   = {/unread},
  annotation = {Read\_Status: New\\
                Read\_Status\_Date: 2025-06-13T09:24:13.832Z},
  file       = {/home/louis/snap/zotero-snap/common/Zotero/storage/7LVQPD6D/Nenna - Lecture 1 Monge and Kantorovich problems from primal to dual.pdf}
}

@article{matchadoNetworkAnalysisMethods2021b,
  title      = {Network Analysis Methods for Studying Microbial Communities: {{A}} Mini Review},
  shorttitle = {Network Analysis Methods for Studying Microbial Communities},
  author     = {Matchado, Monica Steffi and Lauber, Michael and Reitmeier, Sandra and Kacprowski, Tim and Baumbach, Jan and Haller, Dirk and List, Markus},
  year       = {2021},
  month      = jan,
  journal    = {Computational and Structural Biotechnology Journal},
  volume     = {19},
  pages      = {2687--2698},
  issn       = {2001-0370},
  doi        = {10.1016/j.csbj.2021.05.001},
  urldate    = {2025-06-16},
  abstract   = {Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.},
  keywords   = {/unread,Microbial co-occurrence networks,Microbial interactions,Network analysis,Trans-kingdom interactions},
  annotation = {Read\_Status: New\\
                Read\_Status\_Date: 2025-06-16T16:18:09.496Z},
  file       = {/home/louis/snap/zotero-snap/common/Zotero/storage/ZCY74M2I/Matchado et al. - 2021 - Network analysis methods for studying microbial communities A mini review.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/BKZN3MI5/S2001037021001823.html}
}
