Utilities
cynetdiff.utils
¶
Functions used to convert NetworkX graphs to usable models.
check_csr_arrays(starts, edges)
¶
Asserts that the graph represented by starts and edges is in
valid compressed sparse row (CSR) format. Useful for debugging,
before the manual construction of a model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
starts |
array
|
An array of start indices for each node's edges in the edge array. Type
of array elements must be |
required |
edges |
array
|
An array of edges represented as integer indices of nodes. Type
of array elements must be |
required |
Raises:
| Type | Description |
|---|---|
ValueError
|
If the input parameters are not in valid CSR format. |
networkx_to_ic_model(graph, *, activation_prob=None, _include_succcess_prob=False)
¶
Converts a NetworkX graph into an Independent Cascade model. Includes activation
probability values if they are defined on each edge under the key "activation_prob".
Activation probability should only be set on either edges or through the function
argument, not both.
Payoffs for each node are included if they are defined on each node under the key
"payoff".
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
graph |
Graph or DiGraph
|
A NetworkX graph or directed graph. |
required |
activation_prob |
float
|
Activation probability for the Independent Cascade model, by default |
None
|
_include_succcess_prob |
bool
|
If True, includes success probabilities for each edge. These probabilities are then stored in the edge data dictionary with the key "success_prob", by default False. Used mainly for testing. |
False
|
Returns:
| Type | Description |
|---|---|
tuple[IndependentCascadeModel, dict[Any, int]]
|
A tuple with the instance of IndependentCascadeModel using the given graph and a dictionary mapping the nodes of the graph to their integer labels in the model. |
networkx_to_lt_model(graph)
¶
Converts a NetworkX graph into a Linear Threshold model. Includes influence
values if they are defined on each edge under the key "influence".
Payoffs for each node are included if they are defined on each node under the key
"payoff".
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
graph |
Graph or DiGraph
|
A NetworkX graph or directed graph. |
required |
Returns:
| Type | Description |
|---|---|
tuple[LinearThresholdModel, dict[Any, int]]
|
A tuple with the instance of LinearThresholdModel using the given graph and a dictionary mapping the nodes of the graph to their integer labels in the model. |
set_activation_random_sample(graph, weight_set)
¶
Set activation probability on each edge uniformly at random from the given weight set. Should be used on graphs before creating the independent cascade model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
graph |
Graph or DiGraph
|
A NetworkX graph or directed graph. |
required |
weight_set |
AbstractSet[float]
|
The set of weights to sample from. Assigns each edge in the input graph a weight uniformly at random from this set. |
required |
set_activation_uniformly_random(graph, *, range_start=0.0, range_end=1.0)
¶
Set activation probability on each edge uniformly at random in the range
[range_start, range_end]. Must have that
0.0 <= range_start < range_end <= 1.0. Should be used on graphs before
creating the independent cascade model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
graph |
Graph or DiGraph
|
A NetworkX graph or directed graph. |
required |
range_start |
float
|
The start of the range to sample activation probabilities from. If not set,
defaults to |
0.0
|
range_end |
float
|
The end of the range to sample activation probabilities from. If not set,
defaults to |
1.0
|
set_activation_weighted_cascade(graph)
¶
Set activation probability on each edge (u,v) to 1/in_degree(v). Graph must be directed. Should be used on graphs before creating the independent cascade model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
graph |
DiGraph
|
A NetworkX directed graph. |
required |