easygraph package

Subpackages

• easygraph.classes package
  • Submodules
  • easygraph.classes.base module
    • BaseHypergraph
    • load_structure()
  • easygraph.classes.directed_graph module
    • DiGraph
    • DiGraphC
  • easygraph.classes.directed_multigraph module
    • MultiDiGraph
  • easygraph.classes.graph module
    • Graph
    • GraphC
  • easygraph.classes.graphviews module
    • reverse_view()
  • easygraph.classes.hypergraph module
    • Hypergraph
  • easygraph.classes.multigraph module
    • MultiGraph
  • easygraph.classes.operation module
    • add_path()
    • density()
    • number_of_selfloops()
    • selfloop_edges()
    • set_edge_attributes()
    • set_node_attributes()
    • topological_sort()
  • Module contents
• easygraph.datapipe package
  • Submodules
  • easygraph.datapipe.common module
  • easygraph.datapipe.loader module
  • easygraph.datapipe.normalize module
  • Module contents
• easygraph.datasets package
  • Subpackages
    • easygraph.datasets.dynamic package
    • easygraph.datasets.hypergraph package
  • Submodules
  • easygraph.datasets.citation_graph module
    • CitationGraphDataset
    • CiteseerGraphDataset
    • CoraBinary
    • CoraGraphDataset
    • PubmedGraphDataset
    • load_citeseer()
    • load_cora()
    • load_pubmed()
  • easygraph.datasets.get_sample_graph module
    • get_graph_blogcatalog()
    • get_graph_flickr()
    • get_graph_karateclub()
    • get_graph_youtube()
  • easygraph.datasets.gnn_benchmark module
    • AmazonCoBuyComputerDataset
  • easygraph.datasets.graph_dataset_base module
  • easygraph.datasets.karate module
    • KarateClubDataset
  • easygraph.datasets.ppi module
    • LegacyPPIDataset
    • PPIDataset
  • easygraph.datasets.utils module
    • download()
    • extract_archive()
    • generate_mask_tensor()
    • get_download_dir()
    • makedirs()
  • Module contents
• easygraph.experiments package
  • Subpackages
    • easygraph.experiments.hypergraphs package
  • Submodules
  • easygraph.experiments.base module
  • easygraph.experiments.vertex_classification module
  • Module contents
• easygraph.functions package
  • Subpackages
    • easygraph.functions.basic package
    • easygraph.functions.centrality package
    • easygraph.functions.community package
    • easygraph.functions.components package
    • easygraph.functions.core package
    • easygraph.functions.drawing package
    • easygraph.functions.graph_embedding package
    • easygraph.functions.graph_generator package
    • easygraph.functions.hypergraph package
    • easygraph.functions.path package
    • easygraph.functions.structural_holes package
  • Submodules
  • easygraph.functions.isolate module
    • is_isolate()
    • isolates()
    • number_of_isolates()
  • Module contents
• easygraph.ml_metrics package
  • Subpackages
    • easygraph.ml_metrics.hypergraphs package
  • Submodules
  • easygraph.ml_metrics.base module
  • easygraph.ml_metrics.classification module
  • Module contents
• easygraph.model package
  • Subpackages
    • easygraph.model.hypergraphs package
  • Module contents
• easygraph.nn package
  • Subpackages
    • easygraph.nn.convs package
  • Submodules
  • easygraph.nn.loss module
    • BPRLoss
  • easygraph.nn.regularization module
    • EmbeddingRegularization
  • Module contents
• easygraph.readwrite package
  • Subpackages
    • easygraph.readwrite.json_graph package
  • Submodules
  • easygraph.readwrite.edgelist module
    • generate_edgelist()
    • parse_edgelist()
    • read_edgelist()
    • read_weighted_edgelist()
    • write_edgelist()
    • write_weighted_edgelist()
  • easygraph.readwrite.gexf module
    • generate_gexf()
    • read_gexf()
    • relabel_gexf_graph()
    • write_gexf()
  • easygraph.readwrite.gml module
    • generate_gml()
    • parse_gml()
    • read_gml()
    • write_gml()
  • easygraph.readwrite.graphml module
    • GraphML
    • GraphMLReader
    • GraphMLWriter
    • generate_graphml()
    • parse_graphml()
    • read_graphml()
    • write_graphml()
    • write_graphml_lxml()
    • write_graphml_xml()
  • easygraph.readwrite.graphviz module
    • from_agraph()
    • read_dot()
    • to_agraph()
    • write_dot()
  • easygraph.readwrite.pajek module
    • Pajek
    • generate_pajek()
    • parse_pajek()
    • read_pajek()
    • write_pajek()
  • easygraph.readwrite.pickle module
    • read_pickle()
    • write_pickle()
  • easygraph.readwrite.ucinet module
    • UCINET DL
    • generate_ucinet()
    • parse_ucinet()
    • read_ucinet()
    • write_ucinet()
  • Module contents
• easygraph.utils package
  • Submodules
  • easygraph.utils.alias module
    • alias_draw()
    • alias_sample()
    • alias_setup()
    • create_alias_table()
  • easygraph.utils.convert_class module
    • convert_graph_class()
  • easygraph.utils.convert_to_matrix module
    • from_numpy_array()
    • from_pandas_adjacency()
    • from_pandas_edgelist()
    • from_scipy_sparse_matrix()
    • to_numpy_array()
    • to_numpy_matrix()
    • to_scipy_sparse_array()
    • to_scipy_sparse_matrix()
  • easygraph.utils.decorators module
    • hybrid()
    • nodes_or_number()
    • not_implemented_for()
    • only_implemented_for_Directed_graph()
    • only_implemented_for_UnDirected_graph()
    • open_file()
    • retry_method_with_fix()
  • easygraph.utils.download module
  • easygraph.utils.exception module
    • Exceptions
    • EasyGraphError
    • EasyGraphException
    • EasyGraphNotImplemented
    • EasyGraphPointlessConcept
  • easygraph.utils.index_of_node module
    • get_relation_of_index_and_node()
  • easygraph.utils.logging module
  • easygraph.utils.mapped_queue module
    • MappedQueue
  • easygraph.utils.misc module
    • edges_equal()
    • graphs_equal()
    • nodes_equal()
    • pairwise()
    • split()
    • split_len()
  • easygraph.utils.relabel module
    • convert_node_labels_to_integers()
    • relabel_nodes()
  • easygraph.utils.sparse module
    • sparse_dropout()
  • easygraph.utils.type_change module
    • from_pyGraphviz_agraph()
    • to_pyGraphviz_agraph()
  • Module contents

Submodules

easygraph.convert module

easygraph.convert.from_dgl(g) → Graph | DiGraph

Convert a DGL graph to an EasyGraph graph.

Parameters:

g (DGLGraph) – A DGL graph

Raises:

ImportError – If DGL is not installed.

Returns:

Converted EasyGraph graph

Return type:

Union[Graph, DiGraph]

easygraph.convert.from_dict_of_dicts(d, create_using=None, multigraph_input=False)

easygraph.convert.from_dict_of_lists(d, create_using=None)

easygraph.convert.from_edgelist(edgelist, create_using=None)

Returns a graph from a list of edges.

Parameters:

• edgelist (list or iterator) – Edge tuples

• create_using (EasyGraph graph constructor, optional (default=eg.Graph)) – Graph type to create. If graph instance, then cleared before populated.

Examples

>>> edgelist = [(0, 1)]  # single edge (0,1)
>>> G = eg.from_edgelist(edgelist)

or

>>> G = eg.Graph(edgelist)  # use Graph constructor

easygraph.convert.from_networkx(g: nx.Graph | nx.DiGraph) → Graph | DiGraph

Convert a NetworkX graph to an EasyGraph graph.

Parameters:

g (Union[nx.Graph, nx.DiGraph]) – A NetworkX graph

Returns:

Converted EasyGraph graph

Return type:

Union[Graph, DiGraph]

easygraph.convert.from_pyg(data: torch_geometric.data.Data, node_attrs: Iterable[str] | None = None, edge_attrs: Iterable[str] | None = None, graph_attrs: Iterable[str] | None = None, to_undirected: bool | str | None = False, remove_self_loops: bool = False) → Any

Converts a torch_geometric.data.Data instance to a easygraph.Graph if to_undirected is set to True, or a directed easygraph.DiGraph otherwise.

Parameters:

• data (torch_geometric.data.Data) – The data object.

• node_attrs (iterable of str, optional) – The node attributes to be copied. (default: None)

• edge_attrs (iterable of str, optional) – The edge attributes to be copied. (default: None)

• graph_attrs (iterable of str, optional) – The graph attributes to be copied. (default: None)

• to_undirected (bool or str, optional) – If set to True or “upper”, will return a easygraph.Graph instead of a easygraph.DiGraph. The undirected graph will correspond to the upper triangle of the corresponding adjacency matrix. Similarly, if set to “lower”, the undirected graph will correspond to the lower triangle of the adjacency matrix. (default: False)

• remove_self_loops (bool, optional) – If set to True, will not include self loops in the resulting graph. (default: False)

Examples

>>> import torch_geometric as pyg

>>> Data = pyg.data.Data  # type: ignore
>>> edge_index = torch.tensor([
...     [0, 1, 1, 2, 2, 3],
...     [1, 0, 2, 1, 3, 2],
... ])
>>> data = Data(edge_index=edge_index, num_nodes=4)
>>> from_pyg(data)
<easygraph.classes.digraph.DiGraph at 0x2713fdb40d0>

easygraph.convert.to_dgl(g: Graph | DiGraph)

Convert an EasyGraph graph to a DGL graph.

Parameters:

g (Union[Graph, DiGraph]) – An EasyGraph graph

Raises:

ImportError – If DGL is not installed.

Returns:

Converted DGL graph

Return type:

DGLGraph

easygraph.convert.to_easygraph_graph(data, create_using=None, multigraph_input=False)

Make a EasyGraph graph from a known data structure.

The preferred way to call this is automatically from the class constructor

>>> d = {0: {1: {"weight": 1}}}  # dict-of-dicts single edge (0,1)
>>> G = eg.Graph(d)

instead of the equivalent

>>> G = eg.from_dict_of_dicts(d)

Parameters:

• data (object to be converted) –

  Current known types are:

  any EasyGraph graph dict-of-dicts dict-of-lists container (e.g. set, list, tuple) of edges iterator (e.g. itertools.chain) that produces edges generator of edges Pandas DataFrame (row per edge) numpy matrix numpy ndarray scipy sparse matrix pygraphviz agraph

• create_using (EasyGraph graph constructor, optional (default=eg.Graph)) – Graph type to create. If graph instance, then cleared before populated.

• multigraph_input (bool (default False)) – If True and data is a dict_of_dicts, try to create a multigraph assuming dict_of_dict_of_lists. If data and create_using are both multigraphs then create a multigraph from a multigraph.

easygraph.convert.to_networkx(g: Graph | DiGraph) → nx.Graph | nx.DiGraph

Convert an EasyGraph to a NetworkX graph.

Parameters:

g (Union[Graph, DiGraph]) – An EasyGraph graph

Raises:

ImportError is raised if NetworkX is not installed. –

Returns:

Converted NetworkX graph

Return type:

Union[nx.Graph, nx.DiGraph]

easygraph.convert.to_pyg(G: Any, group_node_attrs: List[str] | all | None = None, group_edge_attrs: List[str] | all | None = None) → torch_geometric.data.Data

Converts a easygraph.Graph or easygraph.DiGraph to a torch_geometric.data.Data instance.

Parameters:

• G (easygraph.Graph or easygraph.DiGraph) – A easygraph graph.

• group_node_attrs (List[str] or all, optional) – The node attributes to be concatenated and added to data.x. (default: None)

• group_edge_attrs (List[str] or all, optional) – The edge attributes to be concatenated and added to data.edge_attr. (default: None)

Note

All group_node_attrs and group_edge_attrs values must be numeric.

Examples

>>> import torch_geometric as pyg

>>> pyg_to_networkx = pyg.utils.convert.to_networkx  # type: ignore
>>> networkx_to_pyg = pyg.utils.convert.from_networkx  # type: ignore
>>> Data = pyg.data.Data  # type: ignore
>>> edge_index = torch.tensor([
...     [0, 1, 1, 2, 2, 3],
...     [1, 0, 2, 1, 3, 2],
... ])
>>> data = Data(edge_index=edge_index, num_nodes=4)
>>> g = pyg_to_networkx(data)
>>> # A `Data` object is returned
>>> to_pyg(g)
Data(edge_index=[2, 6], num_nodes=4)

easygraph.exception module

Exceptions

Base exceptions and errors for EasyGraph.

exception easygraph.exception.EasyGraphError

Bases: EasyGraphException

Exception for a serious error in EasyGraph

exception easygraph.exception.EasyGraphException

Bases: Exception

Base class for exceptions in EasyGraph.

exception easygraph.exception.EasyGraphNotImplemented

Bases: EasyGraphException

Exception raised by algorithms not implemented for a type of graph.

exception easygraph.exception.EasyGraphPointlessConcept

Bases: EasyGraphException

Raised when a null graph is provided as input to an algorithm that cannot use it.

The null graph is sometimes considered a pointless concept [1], thus the name of the exception.

References

[1]

Harary, F. and Read, R. “Is the Null Graph a Pointless Concept?” In Graphs and Combinatorics Conference, George Washington University. New York: Springer-Verlag, 1973.