Skip to content

temporal_graph

TemporalGraph

Bases: pathpyG.Graph

Source code in src/pathpyG/core/temporal_graph.py 
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
class TemporalGraph(Graph):
    def __init__(self, data: Data, mapping: IndexMap | None = None) -> None:
        """Creates an instance of a temporal graph from a `TemporalData` object.

        Args:
            data: xxx
            mapping: xxx

        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)
            ```
        """
        if not isinstance(data.edge_index, EdgeIndex):
            data.edge_index = data.edge_index = EdgeIndex(
                data=data.edge_index.contiguous(), sparse_size=(data.num_nodes, data.num_nodes)
            )

        # reorder temporal data
        # TODO: Fix in PyG
        if data.num_nodes != data.num_edges:
            self.data = data.sort_by_time()
        else:
            sorted_idx = torch.argsort(data.time)
            data.time = data.time[sorted_idx]
            for edge_attr in data.edge_attrs():
                if edge_attr == "edge_index":
                    data.edge_index = data.edge_index[:, sorted_idx]
                else:
                    data[edge_attr] = data[edge_attr][sorted_idx]
            self.data = data

        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([e for e in self.data.edge_index.t()])
        }

        self.start_time = self.data.time[0].item()
        self.end_time = self.data.time[-1].item()

    @staticmethod
    def from_edge_list(edge_list, num_nodes: Optional[int] = None) -> TemporalGraph:
        edge_array = np.array(edge_list)
        ts = edge_array[:, 2].astype(np.number)

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

        if not num_nodes:
            num_nodes = index_map.num_ids()

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

    @property
    def temporal_edges(self) -> Generator[Tuple[int, int, int], None, None]:
        """Iterator that yields each edge as a tuple of source and destination node as well as the corresponding timestamp."""
        return [(*self.mapping.to_ids(e), t.item()) for e, t in zip(self.data.edge_index.t(), self.data.time)]

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

    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. It applies [`ToUndirected`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.transforms.ToUndirected.html#torch_geometric.transforms.ToUndirected)
        transform to the underlying [`torch_geometric.Data`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Data.html#torch_geometric.data.Data) object, which automatically
        duplicates edge attributes for newly created directed 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)
            ```
        """
        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 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."""

        return TemporalGraph(
            data=Data(edge_index=self.data.edge_index[:, start_idx:end_idx], time=self.data.time[start_idx:end_idx]),
            mapping=self.mapping,
        )

    def get_window(self, start_time: int, end_time: int) -> TemporalGraph:
        """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"""

        return TemporalGraph(data=self.data.snapshot(start_time, end_time), mapping=self.mapping)

    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 = {"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

order property

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

temporal_edges property

Iterator that yields each edge as a tuple of source and destination node as well as the corresponding timestamp.

__init__

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

Parameters:

Name Type Description Default
data torch_geometric.data.Data

xxx

 required
mapping pathpyG.core.index_map.IndexMap | None

xxx

 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 
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
def __init__(self, data: Data, mapping: IndexMap | None = None) -> None:
    """Creates an instance of a temporal graph from a `TemporalData` object.

    Args:
        data: xxx
        mapping: xxx

    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)
        ```
    """
    if not isinstance(data.edge_index, EdgeIndex):
        data.edge_index = data.edge_index = EdgeIndex(
            data=data.edge_index.contiguous(), sparse_size=(data.num_nodes, data.num_nodes)
        )

    # reorder temporal data
    # TODO: Fix in PyG
    if data.num_nodes != data.num_edges:
        self.data = data.sort_by_time()
    else:
        sorted_idx = torch.argsort(data.time)
        data.time = data.time[sorted_idx]
        for edge_attr in data.edge_attrs():
            if edge_attr == "edge_index":
                data.edge_index = data.edge_index[:, sorted_idx]
            else:
                data[edge_attr] = data[edge_attr][sorted_idx]
        self.data = data

    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([e for e in self.data.edge_index.t()])
    }

    self.start_time = self.data.time[0].item()
    self.end_time = self.data.time[-1].item()

__str__

Return a string representation of the graph

Source code in src/pathpyG/core/temporal_graph.py 
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
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 = {"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

get_batch

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.

Source code in src/pathpyG/core/temporal_graph.py 
147
148
149
150
151
152
153
154
155
def get_batch(self, start_idx: int, end_idx: int) -> TemporalGraph:
    """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."""

    return TemporalGraph(
        data=Data(edge_index=self.data.edge_index[:, start_idx:end_idx], time=self.data.time[start_idx:end_idx]),
        mapping=self.mapping,
    )

get_window

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

Source code in src/pathpyG/core/temporal_graph.py 
157
158
159
160
161
162
def get_window(self, start_time: int, end_time: int) -> TemporalGraph:
    """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"""

    return TemporalGraph(data=self.data.snapshot(start_time, end_time), 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 
96
97
98
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_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 
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
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. It applies ToUndirected transform to the underlying torch_geometric.Data object, which automatically duplicates edge attributes for newly created directed 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 
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
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. It applies [`ToUndirected`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.transforms.ToUndirected.html#torch_geometric.transforms.ToUndirected)
    transform to the underlying [`torch_geometric.Data`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Data.html#torch_geometric.data.Data) object, which automatically
    duplicates edge attributes for newly created directed 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)
        ```
    """
    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)