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temporal_graph

Temporal Graph class for handling time-stamped edges.

TemporalGraph

Bases: pathpyG.Graph

Class representing a temporal graph with time-stamped edges.

Info

The data attribute is a PyG Data object that contains the following attributes:

  • edge_index: Edge index tensor of shape (2, num_edges) representing directed edges.
  • time: Tensor of shape (num_edges,) containing timestamps for each edge.

Attributes:

Name Type Description
data torch_geometric.data.Data

PyG Data object containing temporal edges and attributes.
mapping pathpyG.core.index_map.IndexMap

Mapping from node IDs to indices.
edge_to_index dict

Mapping from edge tuples to their indices.
tedge_to_index dict

Mapping from temporal edge tuples to their indices.
Source code in src/pathpyG/core/temporal_graph.py 
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class TemporalGraph(Graph):
    """Class representing a temporal graph with time-stamped edges.

    Info:
        The [`data`][torch_geometric.data.Data] attribute is a PyG Data object that contains the following attributes:

        - `edge_index`: [Edge index][torch_geometric.EdgeIndex] tensor of shape `(2, num_edges)` representing directed edges.
        - `time`: [Tensor][torch.Tensor] of shape `(num_edges,)` containing timestamps for each edge.

    Attributes:
        data (Data): PyG Data object containing temporal edges and attributes.
        mapping (IndexMap): Mapping from node IDs to indices.
        edge_to_index (dict): Mapping from edge tuples to their indices.
        tedge_to_index (dict): Mapping from temporal edge tuples to their indices.
    """

    def __init__(self, data: Data, mapping: IndexMap | None = None) -> None:
        """Creates an instance of a temporal graph from a `TemporalData` object.

        Args:
            data: PyG `Data` object containing edges saved in `edge_index` and timestamps in `time`.
            mapping: Optional mapping from node IDs to indices.

        Example:
            ```py
            from pytorch_geometric.data import TemporalData
            import pathpyG as pp

            d = Data(edge_index=[[0, 0, 1], [1, 2, 2]], time=[0, 1, 2])
            t = pp.TemporalGraph(d, mapping)
            print(t)
            ```
        """
        self.data = data
        if not isinstance(self.data.edge_index, EdgeIndex):
            self.data.edge_index = EdgeIndex(
                data=self.data.edge_index.contiguous(), sparse_size=(self.data.num_nodes, self.data.num_nodes)
            )

        # reorder temporal data
        # Note that we do not use `torch_geometric.self.data.Data.sort_by_time` because it cannot sort numpy arrays`
        sorted_idx = torch.argsort(self.data.time)
        for edge_attr in set(self.data.edge_attrs()).union(set(["time"])):
            if edge_attr == "edge_index":
                self.data.edge_index = self.data.edge_index[:, sorted_idx]
            else:
                self.data[edge_attr] = self.data[edge_attr][sorted_idx]

        if mapping is not None:
            self.mapping = mapping
        else:
            self.mapping = IndexMap()

        # create mapping between edge index and edge tuples
        self.edge_to_index = {(e[0].item(), e[1].item()): i for i, e in enumerate(self.data.edge_index.t())}
        self.tedge_to_index = {
            (e[0].item(), e[1].item(), t.item()): i
            for i, (e, t) in enumerate(zip([e for e in self.data.edge_index.t()], self.data.time))
        }

    @staticmethod
    def from_edge_list(  # type: ignore[override]
        edge_list, num_nodes: Optional[int] = None, device: Optional[torch.device] = None
    ) -> "TemporalGraph":
        """Create a temporal graph from a list of tuples containing edges with timestamps.

        Args:
            edge_list: A list of tuples in the format (source, destination, timestamp).
            num_nodes: Optional number of nodes in the graph. If not provided, it will be inferred.
            device: The device on which to create the tensors (CPU or GPU).

        Returns:
            TemporalGraph: An instance of the TemporalGraph class.

        Examples:
            Create a temporal graph from an edge list:

            >>> import pathpyG as pp
            >>> edge_list = [("a", "b", 1), ("b", "c", 2), ("c", "a", 3)]
            >>> g = pp.TemporalGraph.from_edge_list(edge_list)
        """
        if len(edge_list) == 0:
            return TemporalGraph(
                data=Data(
                    edge_index=torch.empty((2, 0), dtype=torch.long, device=device),
                    time=torch.empty((0,), dtype=torch.long, device=device),
                    num_nodes=num_nodes,
                ),
            )

        edge_array = np.array(edge_list)

        # Convert timestamps to tensor
        if isinstance(edge_list[0][2], int):
            ts = torch.tensor(edge_array[:, 2].astype(np.int_), device=device)
        else:
            ts = torch.tensor(edge_array[:, 2].astype(np.double), device=device)

        index_map = IndexMap(np.unique(edge_array[:, :2]))
        edge_index = index_map.to_idxs(edge_array[:, :2].T, device=device)

        if not num_nodes:
            num_nodes = index_map.num_ids()

        return TemporalGraph(
            data=Data(
                edge_index=edge_index,
                time=ts,
                num_nodes=num_nodes,
            ),
            mapping=index_map,
        )

    @property
    def temporal_edges(self) -> list:
        """Return all temporal edges as a list of tuples (source, destination, timestamp).

        Returns:
            list: A list of tuples representing temporal edges in the format (source, destination, timestamp).

        Examples:
            Get the list of temporal edges:

            >>> import pathpyG as pp
            >>> g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
            >>> print(g.temporal_edges)
            [('a', 'b', 1), ('b', 'c', 2), ('c', 'a', 3)]

            Iterate over temporal edges:
            >>> for edge in g.temporal_edges:
            ...     print(edge)
            ('a', 'b', 1)
            ('b', 'c', 2)
            ('c', 'a', 3)
        """
        edge_ids = self.mapping.to_ids(self.data.edge_index)
        if isinstance(edge_ids, torch.Tensor):
            edge_ids = to_numpy(edge_ids)
        edge_ids = edge_ids.tolist()
        times = to_numpy(self.data.time).tolist()
        return list(zip(edge_ids[0], edge_ids[1], times))

    def to(self, device: torch.device) -> "TemporalGraph":
        """Moves all graph data to the specified device (CPU or GPU).

        Args:
            device: The target device to move the graph data to.

        Returns:
            TemporalGraph: A new TemporalGraph instance with data on the specified device.
        """
        self.data.edge_index = self.data.edge_index.to(device)
        self.data.time = self.data.time.to(device)
        for attr in self.node_attrs():
            if isinstance(self.data[attr], torch.Tensor):
                self.data[attr] = self.data[attr].to(device)
        for attr in self.edge_attrs():
            if isinstance(self.data[attr], torch.Tensor):
                self.data[attr] = self.data[attr].to(device)
        return self

    @property
    def order(self) -> int:
        """Return order 1, since all temporal graphs must be order one."""
        return 1

    @property
    def start_time(self) -> Union[int, float]:
        """Return the timestamp of the first event in the temporal graph."""
        return self.data.time.min().item()

    @property
    def end_time(self) -> Union[int, float]:
        """Return the timestamp of the last event in the temporal graph."""
        return self.data.time.max().item()

    def shuffle_time(self) -> None:
        """Randomly shuffle the temporal order of edges by randomly permuting timestamps."""
        self.data.time = self.data.time[torch.randperm(len(self.data.time))]

    def to_static_graph(self, weighted: bool = False, time_window: Optional[Tuple[int, int]] = None) -> Graph:
        """Return weighted time-aggregated instance of [`Graph`][pathpyG.Graph] graph.

        Args:
            weighted: whether or not to return a weighted time-aggregated graph
            time_window: A tuple with start and end time of the aggregation window

        Returns:
            Graph: A static graph object
        """
        if time_window is not None:
            idx = (self.data.time >= time_window[0]).logical_and(self.data.time < time_window[1]).nonzero().ravel()
            edge_index = self.data.edge_index[:, idx]
        else:
            edge_index = self.data.edge_index

        n = edge_index.max().item() + 1

        if weighted:
            i, w = torch_geometric.utils.coalesce(
                edge_index.as_tensor(), torch.ones(edge_index.size(1), device=self.data.edge_index.device)
            )
            return Graph(Data(edge_index=EdgeIndex(data=i, sparse_size=(n, n)), edge_weight=w), self.mapping)
        else:
            return Graph.from_edge_index(EdgeIndex(data=edge_index, sparse_size=(n, n)), self.mapping)

    def to_undirected(self) -> "TemporalGraph":
        """Return an undirected version of a directed graph.

        This method transforms the current graph instance into an undirected graph by
        adding all directed edges in opposite direction.

        Warning:
            This method duplicates all temporal edges in the graph, which can lead to duplicated
            edges if the original graph already contains bidirectional edges. As of now, edge attributes will
            **not** be duplicated for the new edges.

        Example:
            ```py
            import pathpyG as pp

            g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
            g_u = g.to_undirected()
            print(g_u)
            ```
        """
        # TODO: Handle edge attributes for new edges
        rev_edge_index = self.data.edge_index.flip([0])
        edge_index = torch.cat([self.data.edge_index, rev_edge_index], dim=1)
        times = torch.cat([self.data.time, self.data.time])
        return TemporalGraph(data=Data(edge_index=edge_index, time=times), mapping=self.mapping)

    def get_batch(self, start_idx: int, end_idx: int) -> "TemporalGraph":
        """Return a batch of temporal edges based on start and end indices.

        Return an instance of the TemporalGraph that captures all time-stamped
        edges in a given batch defined by start and (non-inclusive) end, where start
        and end refer to the index of the first and last event in the time-ordered list of events.

        Args:
            start_idx: The starting index of the batch (inclusive).
            end_idx: The ending index of the batch (exclusive).

        Examples:
            Get a batch of temporal edges:

            >>> import pathpyG as pp
            >>> g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
            >>> batch = g.get_batch(0, 2)
            >>> print(batch.temporal_edges)
            [('a', 'b', 1), ('b', 'c', 2)]
        """
        # Create new Data object with the selected batch of edges and times
        data = Data(edge_index=self.data.edge_index[:, start_idx:end_idx], time=self.data.time[start_idx:end_idx])

        # Copy all node attributes
        for node_attr in self.node_attrs():
            data[node_attr] = self.data[node_attr]
        # Copy only edge attributes for the selected batch
        for edge_attr in self.edge_attrs():
            data[edge_attr] = self.data[edge_attr][start_idx:end_idx]

        return TemporalGraph(
            data=data,
            mapping=self.mapping,
        )

    def get_window(self, start_time: int, end_time: int) -> "TemporalGraph":
        """Return a time window of temporal edges based on start and end timestamps.

        Return an instance of the TemporalGraph that captures all time-stamped
        edges in a given time window defined by start and (non-inclusive) end, where start
        and end refer to the time stamps.

        Args:
            start_time: The starting timestamp of the window (inclusive).
            end_time: The ending timestamp of the window (exclusive).

        Examples:
            Get a time window of temporal edges:

            >>> import pathpyG as pp
            >>> g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
            >>> window = g.get_window(0, 2)
            >>> print(window.temporal_edges)
            [('a', 'b', 1)]
        """
        # While there is a PyG function `Data.snapshot`,
        # we do it manually since it cannot handle numpy arrays as edge attributes.
        edge_mask = (self.data.time >= start_time).logical_and(self.data.time < end_time)
        # Create a new Data object with the selected edges and times
        data = Data(
            edge_index=self.data.edge_index[:, edge_mask],
            time=self.data.time[edge_mask],
        )
        # Copy all node attributes
        for node_attr in self.node_attrs():
            data[node_attr] = self.data[node_attr]
        # Copy only edge attributes for the selected edges
        for edge_attr in self.edge_attrs():
            data[edge_attr] = self.data[edge_attr][edge_mask]

        return TemporalGraph(data=data, mapping=self.mapping)

    def __getitem__(self, key: Union[tuple, str]) -> Any:
        """Return node, edge, temporal edge, or graph attribute.

        Args:
            key: name of attribute to be returned
        """
        if not isinstance(key, tuple):
            if key in self.data.keys():
                return self.data[key]
            else:
                raise KeyError(key + " is not a graph attribute")
        elif key[0] in self.node_attrs():
            return self.data[key[0]][self.mapping.to_idx(key[1])]
        elif key[0] in self.edge_attrs():
            # TODO: Get item for non-temporal edges will only return the last occurence of the edge
            #       This is a limitation and should be fixed in the future.
            if len(key) == 3:
                return self.data[key[0]][self.edge_to_index[self.mapping.to_idx(key[1]), self.mapping.to_idx(key[2])]]
            else:
                return self.data[key[0]][
                    self.tedge_to_index[self.mapping.to_idx(key[1]), self.mapping.to_idx(key[2]), key[3]]
                ]
        else:
            raise KeyError(key[0] + " is not a node or edge attribute")

    def __str__(self) -> str:
        """Return a string representation of the graph."""
        s = "Temporal Graph with {0} nodes, {1} unique edges and {2} events in [{3}, {4}]\n".format(
            self.data.num_nodes,
            self.data.edge_index.unique(dim=1).size(dim=1),
            self.data.edge_index.size(1),
            self.start_time,
            self.end_time,
        )

        attr = self.data.to_dict()
        attr_types = {}
        for k in attr:
            t = type(attr[k])
            if t == torch.Tensor:
                attr_types[k] = str(t) + " -> " + str(attr[k].size())
            else:
                attr_types[k] = str(t)

        from pprint import pformat

        attribute_info: dict[str, dict[str, Any]] = {
            "Node Attributes": {},
            "Edge Attributes": {},
            "Graph Attributes": {},
        }
        for a in self.node_attrs():
            attribute_info["Node Attributes"][a] = attr_types[a]
        for a in self.edge_attrs():
            attribute_info["Edge Attributes"][a] = attr_types[a]
        for a in self.data.keys():
            if not self.data.is_node_attr(a) and not self.data.is_edge_attr(a):
                attribute_info["Graph Attributes"][a] = attr_types[a]
        s += pformat(attribute_info, indent=4, width=160)
        return s

end_time property

Return the timestamp of the last event in the temporal graph.

order property

Return order 1, since all temporal graphs must be order one.

start_time property

Return the timestamp of the first event in the temporal graph.

temporal_edges property

Return all temporal edges as a list of tuples (source, destination, timestamp).

Returns:

Name Type Description
list list

A list of tuples representing temporal edges in the format (source, destination, timestamp).

Examples:

Get the list of temporal edges:

>>> import pathpyG as pp
>>> g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
>>> print(g.temporal_edges)
[('a', 'b', 1), ('b', 'c', 2), ('c', 'a', 3)]

Iterate over temporal edges:

>>> for edge in g.temporal_edges:
...     print(edge)
('a', 'b', 1)
('b', 'c', 2)
('c', 'a', 3)

__getitem__

Return node, edge, temporal edge, or graph attribute.

Parameters:

Name Type Description Default
key typing.Union[tuple, str]

name of attribute to be returned

 required
Source code in src/pathpyG/core/temporal_graph.py 
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def __getitem__(self, key: Union[tuple, str]) -> Any:
    """Return node, edge, temporal edge, or graph attribute.

    Args:
        key: name of attribute to be returned
    """
    if not isinstance(key, tuple):
        if key in self.data.keys():
            return self.data[key]
        else:
            raise KeyError(key + " is not a graph attribute")
    elif key[0] in self.node_attrs():
        return self.data[key[0]][self.mapping.to_idx(key[1])]
    elif key[0] in self.edge_attrs():
        # TODO: Get item for non-temporal edges will only return the last occurence of the edge
        #       This is a limitation and should be fixed in the future.
        if len(key) == 3:
            return self.data[key[0]][self.edge_to_index[self.mapping.to_idx(key[1]), self.mapping.to_idx(key[2])]]
        else:
            return self.data[key[0]][
                self.tedge_to_index[self.mapping.to_idx(key[1]), self.mapping.to_idx(key[2]), key[3]]
            ]
    else:
        raise KeyError(key[0] + " is not a node or edge attribute")

__init__

Creates an instance of a temporal graph from a TemporalData object.

Parameters:

Name Type Description Default
data torch_geometric.data.Data

PyG Data object containing edges saved in edge_index and timestamps in time.

 required
mapping pathpyG.core.index_map.IndexMap | None

Optional mapping from node IDs to indices.

 None
Example 
from pytorch_geometric.data import TemporalData
import pathpyG as pp

d = Data(edge_index=[[0, 0, 1], [1, 2, 2]], time=[0, 1, 2])
t = pp.TemporalGraph(d, mapping)
print(t)
Source code in src/pathpyG/core/temporal_graph.py 
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def __init__(self, data: Data, mapping: IndexMap | None = None) -> None:
    """Creates an instance of a temporal graph from a `TemporalData` object.

    Args:
        data: PyG `Data` object containing edges saved in `edge_index` and timestamps in `time`.
        mapping: Optional mapping from node IDs to indices.

    Example:
        ```py
        from pytorch_geometric.data import TemporalData
        import pathpyG as pp

        d = Data(edge_index=[[0, 0, 1], [1, 2, 2]], time=[0, 1, 2])
        t = pp.TemporalGraph(d, mapping)
        print(t)
        ```
    """
    self.data = data
    if not isinstance(self.data.edge_index, EdgeIndex):
        self.data.edge_index = EdgeIndex(
            data=self.data.edge_index.contiguous(), sparse_size=(self.data.num_nodes, self.data.num_nodes)
        )

    # reorder temporal data
    # Note that we do not use `torch_geometric.self.data.Data.sort_by_time` because it cannot sort numpy arrays`
    sorted_idx = torch.argsort(self.data.time)
    for edge_attr in set(self.data.edge_attrs()).union(set(["time"])):
        if edge_attr == "edge_index":
            self.data.edge_index = self.data.edge_index[:, sorted_idx]
        else:
            self.data[edge_attr] = self.data[edge_attr][sorted_idx]

    if mapping is not None:
        self.mapping = mapping
    else:
        self.mapping = IndexMap()

    # create mapping between edge index and edge tuples
    self.edge_to_index = {(e[0].item(), e[1].item()): i for i, e in enumerate(self.data.edge_index.t())}
    self.tedge_to_index = {
        (e[0].item(), e[1].item(), t.item()): i
        for i, (e, t) in enumerate(zip([e for e in self.data.edge_index.t()], self.data.time))
    }

__str__

Return a string representation of the graph.

Source code in src/pathpyG/core/temporal_graph.py 
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def __str__(self) -> str:
    """Return a string representation of the graph."""
    s = "Temporal Graph with {0} nodes, {1} unique edges and {2} events in [{3}, {4}]\n".format(
        self.data.num_nodes,
        self.data.edge_index.unique(dim=1).size(dim=1),
        self.data.edge_index.size(1),
        self.start_time,
        self.end_time,
    )

    attr = self.data.to_dict()
    attr_types = {}
    for k in attr:
        t = type(attr[k])
        if t == torch.Tensor:
            attr_types[k] = str(t) + " -> " + str(attr[k].size())
        else:
            attr_types[k] = str(t)

    from pprint import pformat

    attribute_info: dict[str, dict[str, Any]] = {
        "Node Attributes": {},
        "Edge Attributes": {},
        "Graph Attributes": {},
    }
    for a in self.node_attrs():
        attribute_info["Node Attributes"][a] = attr_types[a]
    for a in self.edge_attrs():
        attribute_info["Edge Attributes"][a] = attr_types[a]
    for a in self.data.keys():
        if not self.data.is_node_attr(a) and not self.data.is_edge_attr(a):
            attribute_info["Graph Attributes"][a] = attr_types[a]
    s += pformat(attribute_info, indent=4, width=160)
    return s

from_edge_list staticmethod

Create a temporal graph from a list of tuples containing edges with timestamps.

Parameters:

Name Type Description Default
edge_list

A list of tuples in the format (source, destination, timestamp).

 required
num_nodes typing.Optional[int]

Optional number of nodes in the graph. If not provided, it will be inferred.

 None
device typing.Optional[torch.device]

The device on which to create the tensors (CPU or GPU).

 None

Returns:

Name Type Description
TemporalGraph pathpyG.core.temporal_graph.TemporalGraph

An instance of the TemporalGraph class.

Examples:

Create a temporal graph from an edge list:

>>> import pathpyG as pp
>>> edge_list = [("a", "b", 1), ("b", "c", 2), ("c", "a", 3)]
>>> g = pp.TemporalGraph.from_edge_list(edge_list)
Source code in src/pathpyG/core/temporal_graph.py 
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@staticmethod
def from_edge_list(  # type: ignore[override]
    edge_list, num_nodes: Optional[int] = None, device: Optional[torch.device] = None
) -> "TemporalGraph":
    """Create a temporal graph from a list of tuples containing edges with timestamps.

    Args:
        edge_list: A list of tuples in the format (source, destination, timestamp).
        num_nodes: Optional number of nodes in the graph. If not provided, it will be inferred.
        device: The device on which to create the tensors (CPU or GPU).

    Returns:
        TemporalGraph: An instance of the TemporalGraph class.

    Examples:
        Create a temporal graph from an edge list:

        >>> import pathpyG as pp
        >>> edge_list = [("a", "b", 1), ("b", "c", 2), ("c", "a", 3)]
        >>> g = pp.TemporalGraph.from_edge_list(edge_list)
    """
    if len(edge_list) == 0:
        return TemporalGraph(
            data=Data(
                edge_index=torch.empty((2, 0), dtype=torch.long, device=device),
                time=torch.empty((0,), dtype=torch.long, device=device),
                num_nodes=num_nodes,
            ),
        )

    edge_array = np.array(edge_list)

    # Convert timestamps to tensor
    if isinstance(edge_list[0][2], int):
        ts = torch.tensor(edge_array[:, 2].astype(np.int_), device=device)
    else:
        ts = torch.tensor(edge_array[:, 2].astype(np.double), device=device)

    index_map = IndexMap(np.unique(edge_array[:, :2]))
    edge_index = index_map.to_idxs(edge_array[:, :2].T, device=device)

    if not num_nodes:
        num_nodes = index_map.num_ids()

    return TemporalGraph(
        data=Data(
            edge_index=edge_index,
            time=ts,
            num_nodes=num_nodes,
        ),
        mapping=index_map,
    )

get_batch

Return a batch of temporal edges based on start and end indices.

Return an instance of the TemporalGraph that captures all time-stamped edges in a given batch defined by start and (non-inclusive) end, where start and end refer to the index of the first and last event in the time-ordered list of events.

Parameters:

Name Type Description Default
start_idx int

The starting index of the batch (inclusive).

 required
end_idx int

The ending index of the batch (exclusive).

 required

Examples:

Get a batch of temporal edges:

>>> import pathpyG as pp
>>> g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
>>> batch = g.get_batch(0, 2)
>>> print(batch.temporal_edges)
[('a', 'b', 1), ('b', 'c', 2)]
Source code in src/pathpyG/core/temporal_graph.py 
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def get_batch(self, start_idx: int, end_idx: int) -> "TemporalGraph":
    """Return a batch of temporal edges based on start and end indices.

    Return an instance of the TemporalGraph that captures all time-stamped
    edges in a given batch defined by start and (non-inclusive) end, where start
    and end refer to the index of the first and last event in the time-ordered list of events.

    Args:
        start_idx: The starting index of the batch (inclusive).
        end_idx: The ending index of the batch (exclusive).

    Examples:
        Get a batch of temporal edges:

        >>> import pathpyG as pp
        >>> g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
        >>> batch = g.get_batch(0, 2)
        >>> print(batch.temporal_edges)
        [('a', 'b', 1), ('b', 'c', 2)]
    """
    # Create new Data object with the selected batch of edges and times
    data = Data(edge_index=self.data.edge_index[:, start_idx:end_idx], time=self.data.time[start_idx:end_idx])

    # Copy all node attributes
    for node_attr in self.node_attrs():
        data[node_attr] = self.data[node_attr]
    # Copy only edge attributes for the selected batch
    for edge_attr in self.edge_attrs():
        data[edge_attr] = self.data[edge_attr][start_idx:end_idx]

    return TemporalGraph(
        data=data,
        mapping=self.mapping,
    )

get_window

Return a time window of temporal edges based on start and end timestamps.

Return an instance of the TemporalGraph that captures all time-stamped edges in a given time window defined by start and (non-inclusive) end, where start and end refer to the time stamps.

Parameters:

Name Type Description Default
start_time int

The starting timestamp of the window (inclusive).

 required
end_time int

The ending timestamp of the window (exclusive).

 required

Examples:

Get a time window of temporal edges:

>>> import pathpyG as pp
>>> g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
>>> window = g.get_window(0, 2)
>>> print(window.temporal_edges)
[('a', 'b', 1)]
Source code in src/pathpyG/core/temporal_graph.py 
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def get_window(self, start_time: int, end_time: int) -> "TemporalGraph":
    """Return a time window of temporal edges based on start and end timestamps.

    Return an instance of the TemporalGraph that captures all time-stamped
    edges in a given time window defined by start and (non-inclusive) end, where start
    and end refer to the time stamps.

    Args:
        start_time: The starting timestamp of the window (inclusive).
        end_time: The ending timestamp of the window (exclusive).

    Examples:
        Get a time window of temporal edges:

        >>> import pathpyG as pp
        >>> g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
        >>> window = g.get_window(0, 2)
        >>> print(window.temporal_edges)
        [('a', 'b', 1)]
    """
    # While there is a PyG function `Data.snapshot`,
    # we do it manually since it cannot handle numpy arrays as edge attributes.
    edge_mask = (self.data.time >= start_time).logical_and(self.data.time < end_time)
    # Create a new Data object with the selected edges and times
    data = Data(
        edge_index=self.data.edge_index[:, edge_mask],
        time=self.data.time[edge_mask],
    )
    # Copy all node attributes
    for node_attr in self.node_attrs():
        data[node_attr] = self.data[node_attr]
    # Copy only edge attributes for the selected edges
    for edge_attr in self.edge_attrs():
        data[edge_attr] = self.data[edge_attr][edge_mask]

    return TemporalGraph(data=data, mapping=self.mapping)

shuffle_time

Randomly shuffle the temporal order of edges by randomly permuting timestamps.

Source code in src/pathpyG/core/temporal_graph.py 
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def shuffle_time(self) -> None:
    """Randomly shuffle the temporal order of edges by randomly permuting timestamps."""
    self.data.time = self.data.time[torch.randperm(len(self.data.time))]

to

Moves all graph data to the specified device (CPU or GPU).

Parameters:

Name Type Description Default
device torch.device

The target device to move the graph data to.

 required

Returns:

Name Type Description
TemporalGraph pathpyG.core.temporal_graph.TemporalGraph

A new TemporalGraph instance with data on the specified device.
Source code in src/pathpyG/core/temporal_graph.py 
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def to(self, device: torch.device) -> "TemporalGraph":
    """Moves all graph data to the specified device (CPU or GPU).

    Args:
        device: The target device to move the graph data to.

    Returns:
        TemporalGraph: A new TemporalGraph instance with data on the specified device.
    """
    self.data.edge_index = self.data.edge_index.to(device)
    self.data.time = self.data.time.to(device)
    for attr in self.node_attrs():
        if isinstance(self.data[attr], torch.Tensor):
            self.data[attr] = self.data[attr].to(device)
    for attr in self.edge_attrs():
        if isinstance(self.data[attr], torch.Tensor):
            self.data[attr] = self.data[attr].to(device)
    return self

to_static_graph

Return weighted time-aggregated instance of Graph graph.

Parameters:

Name Type Description Default
weighted bool

whether or not to return a weighted time-aggregated graph

 False
time_window typing.Optional[typing.Tuple[int, int]]

A tuple with start and end time of the aggregation window

 None

Returns:

Name Type Description
Graph pathpyG.Graph

A static graph object
Source code in src/pathpyG/core/temporal_graph.py 
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def to_static_graph(self, weighted: bool = False, time_window: Optional[Tuple[int, int]] = None) -> Graph:
    """Return weighted time-aggregated instance of [`Graph`][pathpyG.Graph] graph.

    Args:
        weighted: whether or not to return a weighted time-aggregated graph
        time_window: A tuple with start and end time of the aggregation window

    Returns:
        Graph: A static graph object
    """
    if time_window is not None:
        idx = (self.data.time >= time_window[0]).logical_and(self.data.time < time_window[1]).nonzero().ravel()
        edge_index = self.data.edge_index[:, idx]
    else:
        edge_index = self.data.edge_index

    n = edge_index.max().item() + 1

    if weighted:
        i, w = torch_geometric.utils.coalesce(
            edge_index.as_tensor(), torch.ones(edge_index.size(1), device=self.data.edge_index.device)
        )
        return Graph(Data(edge_index=EdgeIndex(data=i, sparse_size=(n, n)), edge_weight=w), self.mapping)
    else:
        return Graph.from_edge_index(EdgeIndex(data=edge_index, sparse_size=(n, n)), self.mapping)

to_undirected

Return an undirected version of a directed graph.

This method transforms the current graph instance into an undirected graph by adding all directed edges in opposite direction.

Warning

This method duplicates all temporal edges in the graph, which can lead to duplicated edges if the original graph already contains bidirectional edges. As of now, edge attributes will not be duplicated for the new edges.

Example 
import pathpyG as pp

g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
g_u = g.to_undirected()
print(g_u)
Source code in src/pathpyG/core/temporal_graph.py 
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def to_undirected(self) -> "TemporalGraph":
    """Return an undirected version of a directed graph.

    This method transforms the current graph instance into an undirected graph by
    adding all directed edges in opposite direction.

    Warning:
        This method duplicates all temporal edges in the graph, which can lead to duplicated
        edges if the original graph already contains bidirectional edges. As of now, edge attributes will
        **not** be duplicated for the new edges.

    Example:
        ```py
        import pathpyG as pp

        g = pp.TemporalGraph.from_edge_list([("a", "b", 1), ("b", "c", 2), ("c", "a", 3)])
        g_u = g.to_undirected()
        print(g_u)
        ```
    """
    # TODO: Handle edge attributes for new edges
    rev_edge_index = self.data.edge_index.flip([0])
    edge_index = torch.cat([self.data.edge_index, rev_edge_index], dim=1)
    times = torch.cat([self.data.time, self.data.time])
    return TemporalGraph(data=Data(edge_index=edge_index, time=times), mapping=self.mapping)