DenseDelayGraph

experimental.network_dynamics.graph.DenseDelayGraph(
    weights,
    delays,
    region_labels=None,
    symmetric=None,
)

Dense graph with transmission delays.

Extends DenseGraph with a delay matrix for modeling transmission delays between network nodes. Used for delay differential equations (DDEs).

Args: weights: Weight matrix [n_nodes, n_nodes] delays: Delay matrix [n_nodes, n_nodes] in same units as integration time region_labels: Optional sequence of region labels (list, tuple, or array). If None, defaults to [‘Region_0’, ‘Region_1’, …] symmetric: Whether to treat as symmetric (None = auto-detect)

Attributes

Name	Description
delays	Delay matrix [n_nodes, n_nodes].
max_delay	Maximum delay in the network.

Methods

Name	Description
plot	Plot connectivity matrix, delays, and their distributions.
random	Create a random delay graph with brain-like connectivity.
tree_flatten	Flatten DenseDelayGraph for JAX PyTree.
tree_unflatten	Reconstruct DenseDelayGraph from PyTree data.
verify	Verify delay graph structure.

plot

experimental.network_dynamics.graph.DenseDelayGraph.plot(
    log_scale_weights=False,
    figsize=(12, 10),
)

Plot connectivity matrix, delays, and their distributions.

Args: log_scale_weights: If True, log-transform weights before plotting (helps reveal structure) figsize: Figure size (width, height)

Returns: fig, axes: Matplotlib figure and axes (2x2 grid)

random

experimental.network_dynamics.graph.DenseDelayGraph.random(
    n_nodes,
    sparsity=0.7,
    symmetric=True,
    weight_dist='lognormal',
    max_delay=50.0,
    delay_dist='uniform',
    allow_self_loops=False,
    key=None,
)

Create a random delay graph with brain-like connectivity.

Args: n_nodes: Number of nodes in the network sparsity: Fraction of connections present (0.7 = 70% dense) symmetric: Whether to create undirected (symmetric) connectivity weight_dist: Weight distribution (‘lognormal’, ‘uniform’, or ‘binary’) max_delay: Maximum transmission delay delay_dist: Delay distribution (‘uniform’ or ‘constant’) allow_self_loops: Whether to allow self-connections (diagonal) key: JAX random key (if None, creates one with seed 0)

Returns: DenseDelayGraph with random connectivity and delays

Example: >>> import jax >>> key = jax.random.key(42) >>> graph = DenseDelayGraph.random(n_nodes=10, max_delay=20.0, key=key)

tree_flatten

experimental.network_dynamics.graph.DenseDelayGraph.tree_flatten()

Flatten DenseDelayGraph for JAX PyTree.

tree_unflatten

experimental.network_dynamics.graph.DenseDelayGraph.tree_unflatten(
    aux_data,
    children,
)

Reconstruct DenseDelayGraph from PyTree data.

verify

experimental.network_dynamics.graph.DenseDelayGraph.verify(verbose=True)

Verify delay graph structure.

Additional checks beyond Graph.verify(): - Non-negative delays - Finite delay values - Delay matrix shape consistency