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@tjkessler tjkessler released this 02 Aug 21:17
· 71 commits to main since this release
0.11.0
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Learn about vigilant mode.

Domain-and-structure release on the 0.10 stack: measured topology transfer,
directed hypergraph incidence, Ray Train model DDP, fixed-union presence-mask
churn, teaching traffic forecaster ports, in-repo sheaf / cell-complex MVPs,
GraphVAMP + molecular teaching path, and exact-automorphism isotypic
self-block ties. Homogeneous scientific defaults and the linear latent
operator contract are unchanged.

Added

  • Public measured cross-topology transfer API
    (koopman_graph.analysis.evaluate_topology_transfer /
    TopologyTransferReport) with mandatory pernode control; negative
    transfer advantage is a valid outcome. Tutorial
    examples/37_cross_topology_transfer.ipynb calls the API.
  • Directed hypergraph incidence modes on
    HypergraphKoopmanOperator: zhou_symmetric (default),
    forward_random_walk, dual_random_walk (factory
    koopman_hypergraph_incidence_mode). Forward walk follows Ducournau &
    Bretto (Ducournau2014DirectedHypergraphs); encode/advance orientations
    may differ by design.
  • Optional Ray Train model-DDP fit path
    (koopman_graph.distributed.run_ray_train_fit_loop, [ray] /
    [distributed] with ray[train]). Distinct from
    fit_ensemble_with_ray (ensemble members). Native DDP / Fabric remain
    the recommended multi-GPU defaults; multi-node Ray Train is outside CI.
  • Fixed-union node churn: allow_node_churn + presence_masks at capacity
    (N_{\max}) (typed hetero peers included). Losses ignore inactive nodes;
    matvecs stay at capacity. Tutorial
    examples/41_node_churn_presence_masks.ipynb.
  • Teaching traffic forecaster ports with ForecasterProtocol deviation
    tables: AGCRNBaseline (Bai2020AGCRN), MTGNNBaseline
    (Wu2020MTGNN), STGODEBaseline (Fang2021STGODE; optional
    [baselines-ode]), GraphCastBaseline (Lam2023GraphCast; small-mesh
    weather teaching slice). Tutorial
    examples/42_traffic_teaching_baselines.ipynb.
  • Sheaf and cell-complex encode/decode MVPs (encoder="sheaf" /
    "cell_complex"; Hansen2020SheafNeuralNetworks,
    Bodnar2022NeuralSheaf, Bodnar2021CWNetworks motivation). Same linear
    Koopman head as GNN / simplicial-1. Tutorial
    examples/43_tdl_sheaf_cell_ablation.ipynb.
  • Contact-graph GraphVAMP teaching baseline (GraphVAMPBaseline) with
    synthetic molecular oracle under koopman_graph.datasets.molecular and
    optional deeptime interop (koopman_graph.interop, [msm] / [md]).
    Positioning relative to GraphVAMPnets (Ghorbani2022GraphVAMPNet) /
    VAMPnets (Mardt2018VAMPnets, Wu2020VAMP). Tutorial
    examples/44_graphvamp_md.ipynb.
  • Exact-automorphism isotypic self-block ties
    (koopman_symmetry="isotypic"; compute_isotypic_decomposition;
    textbook (K_n) / (C_n) oracles — Serre1977LinearRepresentations).
    Tutorial examples/45_isotypic_symmetry.ipynb. Neighbor-factor
    ((K_{\mathrm{nbr}})) isotypic tying is not shipped.
  • Sphinx: rewritten limitations.rst / capabilities.rst, architecture
    v0.11 map, FAQ (Ray Train choice, transfer honesty, teaching vs
    leaderboard), API / installation extras, molecular dataset card and
    presence-vs-observation mask semantics, tutorials 37 / 41–45.

Notes

  • Teaching baselines are not LibCity / BasicTS leaderboard reproductions.
    GraphCast is not a PEMS sensor-graph forecaster and not ERA5-scale
    production training.
  • Presence-mask churn is a fixed-union (N_{\max}) MVP — not unbounded
    open-world graph growth.
  • Sheaf / cell MVPs are not full TopologicX / cellular TDL parity. An
    optional richer external-complex bridge was cut for this release.
  • GraphVAMP path is teaching / diagnostic — not Folding@home-scale MD, not
    a PyEMMA replacement. A public alanine-dipeptide loader was cut; CI
    uses the synthetic two-state oracle.
  • Isotypic mode requires exact Aut (pynauty); WL methods are refused. Do
    not read orbit-label ties as isotypic reduction. Sample-efficiency wins
    are not guaranteed.
  • Dense (N\cdot d) (or (N_{\max}\cdot d)) ceilings are unchanged by
    trainers or presence masks.
  • Remaining honesty boundaries (leaderboard protocols, multi-node Ray Train,
    infinite-dimensional ResDMD, equivariant latent (K), full DPK /
    (K^{2})VAE, etc.) are listed in Sphinx limitations without a deferred
    roadmap of the same shipped items.

Full changelog: see CHANGELOG.md ([0.11.0] - 2026-08-02).

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