easygraph package

Subpackages

Submodules

easygraph.convert module

easygraph.convert.from_dgl(g) → Union[Graph, DiGraph][source]

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)[source]
easygraph.convert.from_dict_of_lists(d, create_using=None)[source]
easygraph.convert.from_edgelist(edgelist, create_using=None)[source]

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: Union[nx.Graph, nx.DiGraph]) → Union[Graph, DiGraph][source]

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: Optional[Iterable[str]] = None, edge_attrs: Optional[Iterable[str]] = None, graph_attrs: Optional[Iterable[str]] = None, to_undirected: Optional[Union[bool, str]] = False, remove_self_loops: bool = False) → Any[source]

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: Union[Graph, DiGraph])[source]

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)[source]

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: Union[Graph, DiGraph]) → Union[nx.Graph, nx.DiGraph][source]

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: Optional[Union[List[str], all]] = None, group_edge_attrs: Optional[Union[List[str], all]] = None) → torch_geometric.data.Data[source]

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)

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