dynamicmultiplex 1.1.0

Uncertainty-quantification overhaul, motivated by a large-scale simulation study of empirical CI coverage (planted-partition multilayer networks; n = 50-400 nodes, 3-10 communities, 5-15 layers, 5 switching rates, 3 density regimes, Louvain and Leiden, all similarity couplings).

New features

• New method: fit_multilayer_hungarian(). A two-stage snapshot-and-match tracker that detects communities independently per layer and then aligns their labels across consecutive layers with the Hungarian (optimal linear-sum assignment) algorithm on the community overlap matrix (clue::solve_LSAP; SciPy’s linear_sum_assignment in the Python package). This is the standard “independent detection + optimal label matching” baseline, now exported as a first-class method with the same multilayer_community_fit interface (layer_communities, meta_communities, extract_meta_membership()). It complements the coupling-based fits: those jointly optimise on a coupled supra-graph, this one matches post hoc. interlayer_ties is NULL for this method.

• Cross-layer community tracking (second-stage detection). The fit_multilayer_*() functions now run a second community detection on the interlayer/community graph (the similarity ties plus self-loops) and return meta_communities: the tracked partition grouping per-layer communities into cross-layer meta-communities. This is what makes custom layer_links and the interlayer coupling actually affect the returned membership; previously the output equalled independent per-layer detection. Access it with the new exported extract_meta_membership(). The per-layer layer_communities are unchanged and still returned. For the Jaccard/overlap specifications the second stage runs on the community-similarity graph; for the identity specification it is Mucha (2010) multislice modularity — the layers are stacked into one supra-graph (intra-layer edges = each layer’s own adjacency) with interlayer identity edges, and a single detection is run on the whole supra-graph. Custom layer_links set the coupling in both cases.

• Multislice omega / resolution-limit controls. fit_multilayer_identity_ties() gains an omega argument (Mucha’s interlayer coupling strength) alongside resolution_parameter, both forwarded to the multislice supra-graph detection. omega scales the interlayer identity-edge weights: small values decouple toward independent per-layer detection, omega = 1 recovers the planted partition on well-separated networks, and large values drive the known multislice degeneracy (temporal threads fragment or slices collapse). Together with resolution_parameter (Mucha’s modularity resolution) these expose the omega-sensitivity and resolution-limit behavior of multislice modularity as tunable knobs. Mirrored in the Python package.

• Uncertainty is now quantified on the meta-communities. bootstrap_multilayer(), co_assignment_ci(), and community_est() operate on the cross-layer meta-communities rather than the independent per-layer partitions. Co-assignment now answers “do two nodes belong to the same persistent community,” and the community count is the number of meta-communities per layer. (Coverage of these meta-community intervals must be re-validated in simulation; the earlier per-layer coverage numbers do not transfer.)

• seed argument for reproducible detection. All fit_multilayer_*() functions gain a seed argument. When supplied, the global RNG state is saved, the RNG is seeded for the duration of the call, and the previous state is restored on exit, so seeded detection never disturbs the caller’s random number stream (e.g. bootstrap_multilayer() resampling). The default (NULL) preserves the previous behavior: detection inherits the caller’s RNG stream, so existing scripts using set.seed() are unaffected. (In the Python package the default is seed=123, matching its previous internally fixed detection seed.)

• Node-universe validation. Layers must now share the same node set (compared on vertex names when present, on vertex indices otherwise); unequal universes error with instructions instead of silently misaligning interlayer ties. fit_multilayer_identity_ties() gains allow_unequal_nodes = TRUE to retain its documented support for nodes entering or exiting the system.

Breaking changes

• Layers with different node sets are now rejected by all fit functions (previously assumed aligned without checking). For identity ties, opt back in with allow_unequal_nodes = TRUE.

• animate_multilayer_gif() no longer has a default output_file (previously "multilayer_animation.gif" in the working directory). An explicit path is now required, per CRAN policy that functions must not write to the user’s filespace by default; use tempfile(fileext = ".gif") for a temporary location.

• community_ci() is renamed community_est() and no longer returns a community-count confidence interval. The percentile community_count_ci (estimate/lower/upper) is replaced by a community_count data frame that reports, per layer, the observed-network estimate and its bootstrap reproducibility (the share of replicates whose community count equals the estimate), plus a plain-language report. Reason: the interval covered the truth at ~0.99 on well-specified planted-partition networks but collapsed to ~0.62 under strong community-size skew, and no observable diagnostic separated the reliable cases from the rest. The reproducibility summary is descriptive and makes no coverage claim; the package’s validated interval is co_assignment_ci(). The raw per-replicate community counts are no longer exposed: bootstrap_multilayer() now returns community_count_reproducibility (the per-layer share of replicates matching the observed count) in place of the old community_count_samples vector.

• community_ci() no longer returns modularity_ci. The study showed its empirical coverage is never close to the nominal 95% at any network size (~0.40 at n = 50, 0.00 at n = 100, vacuously 1.00 at n = 200): community detection maximizes modularity, so the bootstrap interval concentrates around an optimized, upwardly biased estimate. An interval that is anti-conservative when detection is imperfect and uninformative when it is exact is not a confidence interval; it has been removed rather than documented around. Raw bootstrap draws remain available in bootstrap_multilayer()$modularity_samples for descriptive use.

New features

• bootstrap_multilayer() now uses a parametric network bootstrap: within/between-community edge probabilities and edge-weight pools are estimated from the observed network using the point-estimate partition, and each replicate redraws the full edge set. The previous scheme (Bayesian bootstrap on edge weights with fixed topology) was REMOVED rather than kept as an option: because it never varies which edges exist, it understates the variability of fresh data, and simulation studies showed intervals built from it undercover badly (~45-48% at nominal 95% for pairwise co-assignment) with no recovery as networks grow. Under the project’s design principle, a misleading option is worse than no option.

• co_assignment_ci(): Wilson score intervals for node-pair co-assignment propensities, computed from the bootstrap co-assignment probabilities. Label-invariant, so it avoids the label-switching problem that makes per-node membership intervals ill-defined.

Improved warnings

• community_est() no longer issues the small-network community-count CI warning. With the count interval removed there is no coverage claim to qualify; the reproducibility summary is descriptive at any network size. The co_assignment_ci() guidance to interpret cautiously below 100 nodes remains.

• directed = TRUE with algorithm = "leiden" previously errored (igraph’s cluster_leiden() supports undirected graphs only). Directed layers are now collapsed to weighted undirected graphs — matching the existing Louvain behavior — with a single warning per fit pointing users who need directed-aware detection to the Python package (leidenalg).

dynamicmultiplex 1.0.0

Initial CRAN release.

• Multilayer community detection via Louvain or Leiden on evolving networks, with interlayer coupling built from Jaccard similarity, overlap coefficient, node-strength weighted variants, or direct node identity links (fit_multilayer_jaccard(), fit_multilayer_overlap(), fit_multilayer_weighted_jaccard(), fit_multilayer_weighted_overlap(), fit_multilayer_identity_ties()).
• User-specified layer connectivity via layer_links, enabling adjacent-only temporal coupling.
• Explicit objective selection (CPM or modularity) via the objective argument; negative edge weights are rejected for modularity-based methods.
• Bayesian bootstrap confidence intervals for community assignments (bootstrap_multilayer(), community_ci()).
• Simulation utilities for benchmarking (simulate_and_fit_multilayer()).
• Visualization helpers: layer series plots (plot_multilayer_series()), alluvial diagrams (plot_multilayer_alluvial()), and animated GIFs (animate_multilayer_gif()).