corneto.graph.Graph#

class corneto.graph.Graph(default_edge_type=EdgeType.DIRECTED, **kwargs)#

Bases: BaseGraph

Concrete graph implementation supporting directed/undirected edges and hyperedges.

Allows parallel edges (multiple edges between same vertices) and hyperedges (edges connecting multiple vertices). Edges and vertices can have attributes.

Examples

>>> graph = corneto.Graph()
>>> graph.add_edge(1, 2)
>>> graph.plot()

Parameters:: default_edge_type (EdgeType)

__init__(default_edge_type=EdgeType.DIRECTED, **kwargs)#

Initialize Graph.

Parameters:

default_edge_type (EdgeType) – Default type for new edges
**kwargs – Additional graph attributes

Return type:

None

Methods

`__init__`([default_edge_type])	Initialize Graph.
`add_edge`(source, target[, type, ...])	Add edge to the graph.
`add_edges`(edges[, type])	Add multiple edges to the graph.
`add_vertex`(v, **kwargs)	Add vertex to the graph.
`add_vertices`(vertices, **kwargs)	Add multiple vertices to the graph.
`bfs`(starting_vertices[, reverse, undirected])	Perform breadth-first search (BFS) traversal.
`copy`()	Create deep copy of graph.
`edge_subgraph`(edges)	Create subgraph induced by a set of edges.
`edges`([vertices])	Get edges in the graph.
`extract_subgraph`([vertices, edges])	Extract subgraph induced by vertices and/or edges.
`filter_graph`([filter_vertex, filter_edge])	Create filtered graph based on vertex and edge predicates.
`from_cobra_model`(model)	Create graph from COBRA metabolic model.
`from_dict`(data)	Create a graph from a dictionary representation.
`from_json`(json_str)	Create a graph from a JSON string.
`from_miom_model`(model)	Create graph from MIOM metabolic model.
`from_networkx`(G)	Create graph from NetworkX graph.
`from_sif`(sif_file[, delimiter, has_header, ...])	Create graph from Simple Interaction Format (SIF) file.
`from_sif_tuples`(tuples)	Create graph from iterable of SIF tuples.
`from_tuples`(tuples)	Alias for corneto.io.load_graph_from_sif_tuples for backwards compatibility.
`from_vertex_incidence`(A, vertex_ids, edge_ids)	Create graph from vertex incidence matrix and labels.
`get_attr_edge`(index)	Get attributes of an edge.
`get_attr_edges`([indexes])	Get attributes of multiple edges.
`get_attr_from_edges`(attr[, default])	Get specific attribute from all edges.
`get_attr_vertex`(v)	Get attributes of a vertex.
`get_attr_vertices`([vertices])	Get attributes of multiple vertices.
`get_common_incident_edges`(vertices)	Get common incident edges for multiple vertices.
`get_edge`(index)	Get edge at specified index.
`get_edges`(indexes)	Get multiple edges by their indices.
`get_edges_by_attr`(key, value)	Get edges by specific attribute value.
`get_graph_attributes`()	Get global graph attributes.
`get_incident_edges`(vertices)	Get incident edges for multiple vertices.
`get_vertex`(index)	Get vertex by its index.
`get_vertex_incidence_matrix_as_lists`([values])	Get vertex incidence matrix as lists.
`hash`()	Compute hash of the graph based on its content.
`in_edges`(vertices)	Get incoming edges for vertices.
`is_hypergraph`()	Check if the graph is a hypergraph.
`load`(filename[, compression])	Load graph from a saved file.
`load_json`(filepath[, compression])	Load graph from a JSON file.
`neighbors`(vertex)	Get neighbors of a vertex (ignoring edge direction).
`out_edges`(vertices)	Get outgoing edges for vertices.
`plot`(**kwargs)	Plot the graph using Graphviz.
`plot_values`([vertex_values, edge_values, ...])
`predecessors`(vertices)	Get predecessor vertices for multiple vertices.
`prune`([source, target])	Prune the graph to include only reachable vertices.
`reachability_analysis`(input_nodes, output_nodes)	Perform reachability analysis from input nodes to output nodes.
`reverse`()	Create new graph with all edge directions reversed.
`save`(filename[, compression])	Save graph to file using pickle serialization.
`save_json`(filepath[, indent, compression])	Save graph to a JSON file.
`subgraph`(vertices)	Create subgraph induced by a set of vertices.
`successors`(vertices)	Get successor vertices for multiple vertices.
`to_dict`()	Convert graph to a JSON-serializable dictionary.
`to_dot`(**kwargs)	Convert graph to DOT format.
`to_graphviz`(**kwargs)	Convert graph to Graphviz representation.
`to_json`(**kwargs)	Serialize graph to JSON string.
`toposort`()	Perform topological sort on the graph using Kahn's algorithm.
`vertex_incidence_matrix`([values, sparse])	Get vertex incidence matrix.

Attributes

`E`	Edges in the graph.
`V`	Vertices in the graph.
`ne`	Number of edges in the graph.
`num_edges`	Number of edges in the graph.
`num_vertices`	Number of vertices in the graph.
`nv`	Number of vertices in the graph.
`shape`	Shape of the graph (number of vertices, number of edges).
`vertices`	Vertices in the graph.

get_edge(index)#

Get edge at specified index.

Parameters:: index (int) – Edge index to retrieve
Returns:: Edge tuple (source vertices, target vertices)
Return type:: Tuple[FrozenSet[Any], FrozenSet[Any]]

get_graph_attributes()#

Get global graph attributes.

Returns:: Attributes object containing graph-level properties
Return type:: Attributes

extract_subgraph(vertices=None, edges=None)#

Extract subgraph induced by vertices and/or edges.

Parameters:

vertices (Iterable | None) – Optional vertices to include
edges (Iterable[int] | None) – Optional edge indices to include

Returns:

New Graph containing only specified vertices/edges and their incident edges

reverse()#

Create new graph with all edge directions reversed.

Returns:: New Graph with reversed edges
Return type:: Graph

filter_graph(filter_vertex=None, filter_edge=None)#

Create filtered graph based on vertex and edge predicates.

Parameters:

filter_vertex (Callable[[Any], bool] | None) – Optional function that returns True for vertices to keep
filter_edge (Callable[[int], bool] | None) – Optional function that returns True for edges to keep

Returns:

New Graph containing only elements that pass filters

copy()#

Create deep copy of graph.

Returns:: New Graph with copied structure and attributes
Return type:: Graph

static from_tuples(tuples)#

Alias for corneto.io.load_graph_from_sif_tuples for backwards compatibility.

Parameters:: tuples (Iterable[Tuple]) – Iterable of (source, interaction, target) tuples
Returns:: New Graph created from tuple data

to_dict()#

Convert graph to a JSON-serializable dictionary.

Returns:: Dictionary representation of the graph
Return type:: Dict

to_json(**kwargs)#

Serialize graph to JSON string.

Parameters:: **kwargs – Additional arguments passed to json.dumps()
Returns:: JSON string representation of the graph
Return type:: str

classmethod from_dict(data)#

Create a graph from a dictionary representation.

Parameters:: data (Dict) – Dictionary containing graph data
Returns:: New Graph instance created from the dictionary
Return type:: Graph

classmethod from_json(json_str)#

Create a graph from a JSON string.

Parameters:: json_str (str) – JSON string containing graph data
Returns:: New Graph instance created from the JSON string
Return type:: Graph

static from_networkx(G)#

Create graph from NetworkX graph.

Parameters:: G (NxGraph | NxDiGraph) – NetworkX graph instance
Returns:: Graph instance with equivalent structure

save_json(filepath, indent=None, compression=None)#

Save graph to a JSON file.

Parameters:

filepath (str) – Path where to save the JSON file
indent (int | None) – Optional number of spaces for indentation (for pretty printing)
compression (str | None) – Optional compression format (‘auto’, ‘gzip’, ‘bz2’, ‘xz’, or None)

Raises:

ValueError – If filepath is empty

Return type:

None

classmethod load_json(filepath, compression='auto')#

Load graph from a JSON file.

Parameters:

filepath (str) – Path to the JSON file
compression (str | None) – Optional compression format (‘auto’, ‘gzip’, ‘bz2’, ‘xz’, or None)

Returns:

New Graph instance loaded from the file

Raises:

FileNotFoundError – If the file doesn’t exist

Return type:

Graph

static from_vertex_incidence(A, vertex_ids, edge_ids)#

Create graph from vertex incidence matrix and labels.

Parameters:

A (ndarray) – Vertex incidence matrix. Rows are vertices, columns are edges. Non-zero entries indicate edge-vertex connections.
vertex_ids (List[str] | ndarray) – Labels for vertices corresponding to matrix rows
edge_ids (List[str] | ndarray) – Labels for edges corresponding to matrix columns

Returns:

Graph instance constructed from incidence matrix

Raises:

ValueError – If dimensions of inputs don’t match

static from_sif(sif_file, delimiter='\t', has_header=False, discard_self_loops=True, column_order=[0, 1, 2])#

Create graph from Simple Interaction Format (SIF) file.

Parameters:

sif_file (str) – Path to SIF file
delimiter (str) – Column delimiter in file
has_header (bool) – Whether file has a header row
discard_self_loops (bool | None) – Whether to ignore self-loops
column_order (List[int]) – Order of source, interaction, target columns

Returns:

New Graph loaded from SIF file

static from_sif_tuples(tuples)#

Create graph from iterable of SIF tuples.

Parameters:: tuples (Iterable[Tuple]) – Iterable of (source, interaction, target) tuples
Returns:: New Graph created from SIF data

static from_cobra_model(model)#

Create graph from COBRA metabolic model.

Parameters:: model (CobraModel) – COBRApy model instance
Returns:: New Graph representing the metabolic network

static from_miom_model(model)#

Create graph from MIOM metabolic model.

Parameters:: model – MIOM model instance or path to compressed model file
Returns:: New Graph representing the metabolic network