DifferenceCoupling

experimental.network_dynamics.coupling.DifferenceCoupling(
    incoming_states=None,
    local_states=None,
    **kwargs,
)

Diffusive coupling based on state differences.

Computes coupling based on the difference between incoming and local states, implementing:

\[c_i = G \cdot \sum_{j} w_{ij} (x_j - x_i)\]

This type of coupling is useful for synchronization and consensus dynamics, as it drives nodes toward common states. Automatically uses sparse-optimized computation for sparse graphs.

Parameters

Name	Type	Description	Default
incoming_states	str or list of str	State name(s) to collect from connected nodes (required)	None
local_states	str or list of str	State name(s) from current node (required for computing differences)	None

Attributes

Name	Type	Description
N_OUTPUT_STATES	int	Number of output coupling states: 1
DEFAULT_PARAMS	Bunch	Default parameters: G=1.0 (global coupling strength)

Examples

>>> # Diffusive coupling via 'x' state
>>> coupling = DifferenceCoupling(incoming_states='x', local_states='x', G=1.0)

Methods

Name	Description
post	Apply coupling strength to summed differences.
pre	Compute difference between incoming and local states (per-edge).

post

experimental.network_dynamics.coupling.DifferenceCoupling.post(
    summed_inputs,
    local_states,
    params,
)

Apply coupling strength to summed differences.

Args: summed_inputs: Summed differences [n_inputs, n_nodes] local_states: Local states (not used) params: Bunch with G

Returns: Scaled coupling [n_inputs, n_nodes]

pre

experimental.network_dynamics.coupling.DifferenceCoupling.pre(
    incoming_states,
    local_states,
    params,
)

Compute difference between incoming and local states (per-edge).

Args: incoming_states: States from connected nodes in per-edge format [n_incoming, n_nodes_target, n_nodes_source] local_states: States from current node [n_local, n_nodes] params: Coupling parameters (not used in pre)

Returns: State differences [n_incoming, n_nodes, n_nodes] (per-edge)