FusedCSCSamplingGraph¶

class
dgl.graphbolt.
FusedCSCSamplingGraph
(c_csc_graph: torch.ScriptObject)[source]¶ Bases:
dgl.graphbolt.sampling_graph.SamplingGraph
A sampling graph in CSC format.
Copy the graph to shared memory.
 Parameters
shared_memory_name (str) – Name of the shared memory.
 Returns
The copied FusedCSCSamplingGraph object on shared memory.
 Return type

in_subgraph
(nodes: Union[torch.Tensor, Dict[str, torch.Tensor]]) → dgl.graphbolt.impl.sampled_subgraph_impl.SampledSubgraphImpl[source]¶ Return the subgraph induced on the inbound edges of the given nodes.
An in subgraph is equivalent to creating a new graph using the incoming edges of the given nodes. Subgraph is compacted according to the order of passedin nodes.
 Parameters
nodes (torch.Tensor or Dict[str, torch.Tensor]) –
 IDs of the given seed nodes.
If nodes is a tensor: It means the graph is homogeneous graph, and ids inside are homogeneous ids.
If nodes is a dictionary: The keys should be node type and ids inside are heterogeneous ids.
 Returns
The in subgraph.
 Return type
Examples
>>> import dgl.graphbolt as gb >>> import torch >>> total_num_nodes = 5 >>> total_num_edges = 12 >>> ntypes = {"N0": 0, "N1": 1} >>> etypes = { ... "N0:R0:N0": 0, "N0:R1:N1": 1, "N1:R2:N0": 2, "N1:R3:N1": 3} >>> indptr = torch.LongTensor([0, 3, 5, 7, 9, 12]) >>> indices = torch.LongTensor([0, 1, 4, 2, 3, 0, 1, 1, 2, 0, 3, 4]) >>> node_type_offset = torch.LongTensor([0, 2, 5]) >>> type_per_edge = torch.LongTensor( ... [0, 0, 2, 2, 2, 1, 1, 1, 3, 1, 3, 3]) >>> graph = gb.fused_csc_sampling_graph(indptr, indices, ... node_type_offset=node_type_offset, ... type_per_edge=type_per_edge, ... node_type_to_id=ntypes, ... edge_type_to_id=etypes) >>> nodes = {"N0":torch.LongTensor([1]), "N1":torch.LongTensor([1, 2])} >>> in_subgraph = graph.in_subgraph(nodes) >>> print(in_subgraph.sampled_csc) {'N0:R0:N0': CSCFormatBase(indptr=tensor([0, 0]), indices=tensor([], dtype=torch.int64), ), 'N0:R1:N1': CSCFormatBase(indptr=tensor([0, 1, 2]), indices=tensor([1, 0]), ), 'N1:R2:N0': CSCFormatBase(indptr=tensor([0, 2]), indices=tensor([0, 1]), ), 'N1:R3:N1': CSCFormatBase(indptr=tensor([0, 1, 3]), indices=tensor([0, 1, 2]), )}

pin_memory_
()[source]¶ Copy FusedCSCSamplingGraph to the pinned memory inplace. Returns the same object modified inplace.

sample_layer_neighbors
(nodes: Union[torch.Tensor, Dict[str, torch.Tensor]], fanouts: torch.Tensor, replace: bool = False, probs_name: Optional[str] = None) → dgl.graphbolt.impl.sampled_subgraph_impl.SampledSubgraphImpl[source]¶ Sample neighboring edges of the given nodes and return the induced subgraph via layerneighbor sampling from the NeurIPS 2023 paper LayerNeighbor Sampling – Defusing Neighborhood Explosion in GNNs
 Parameters
nodes (torch.Tensor or Dict[str, torch.Tensor]) –
 IDs of the given seed nodes.
If nodes is a tensor: It means the graph is homogeneous graph, and ids inside are homogeneous ids.
If nodes is a dictionary: The keys should be node type and ids inside are heterogeneous ids.
fanouts (torch.Tensor) –
The number of edges to be sampled for each node with or without considering edge types.
When the length is 1, it indicates that the fanout applies to all neighbors of the node as a collective, regardless of the edge type.
Otherwise, the length should equal to the number of edge types, and each fanout value corresponds to a specific edge type of the nodes.
 The value of each fanout should be >= 0 or = 1.
When the value is 1, all neighbors (with nonzero probability, if weighted) will be sampled once regardless of replacement. It is equivalent to selecting all neighbors with nonzero probability when the fanout is >= the number of neighbors (and replace is set to false).
When the value is a nonnegative integer, it serves as a minimum threshold for selecting neighbors.
replace (bool) – Boolean indicating whether the sample is preformed with or without replacement. If True, a value can be selected multiple times. Otherwise, each value can be selected only once.
probs_name (str, optional) – An optional string specifying the name of an edge attribute. This attribute tensor should contain (unnormalized) probabilities corresponding to each neighboring edge of a node. It must be a 1D floatingpoint or boolean tensor, with the number of elements equalling the total number of edges.
 Returns
The sampled subgraph.
 Return type
Examples
>>> import dgl.graphbolt as gb >>> import torch >>> ntypes = {"n1": 0, "n2": 1} >>> etypes = {"n1:e1:n2": 0, "n2:e2:n1": 1} >>> indptr = torch.LongTensor([0, 2, 4, 6, 7, 9]) >>> indices = torch.LongTensor([2, 4, 2, 3, 0, 1, 1, 0, 1]) >>> node_type_offset = torch.LongTensor([0, 2, 5]) >>> type_per_edge = torch.LongTensor([1, 1, 1, 1, 0, 0, 0, 0, 0]) >>> graph = gb.fused_csc_sampling_graph(indptr, indices, ... node_type_offset=node_type_offset, ... type_per_edge=type_per_edge, ... node_type_to_id=ntypes, ... edge_type_to_id=etypes) >>> nodes = {'n1': torch.LongTensor([0]), 'n2': torch.LongTensor([0])} >>> fanouts = torch.tensor([1, 1]) >>> subgraph = graph.sample_layer_neighbors(nodes, fanouts) >>> print(subgraph.sampled_csc) {'n1:e1:n2': CSCFormatBase(indptr=tensor([0, 1]), indices=tensor([0]), ), 'n2:e2:n1': CSCFormatBase(indptr=tensor([0, 1]), indices=tensor([2]), )}

sample_negative_edges_uniform
(edge_type, node_pairs, negative_ratio)[source]¶ Sample negative edges by randomly choosing negative sourcedestination pairs according to a uniform distribution. For each edge
(u, v)
, it is supposed to generate negative_ratio pairs of negative edges(u, v')
, wherev'
is chosen uniformly from all the nodes in the graph. Asu
is exactly same as the corresponding positive edges, it returns None for negative sources. Parameters
edge_type (str) – The type of edges in the provided node_pairs. Any negative edges sampled will also have the same type. If set to None, it will be considered as a homogeneous graph.
node_pairs (Tuple[Tensor, Tensor]) – A tuple of two 1D tensors that represent the source and destination of positive edges, with ‘positive’ indicating that these edges are present in the graph. It’s important to note that within the context of a heterogeneous graph, the ids in these tensors signify heterogeneous ids.
negative_ratio (int) – The ratio of the number of negative samples to positive samples.
 Returns
A tuple consisting of two 1D tensors represents the source and destination of negative edges. In the context of a heterogeneous graph, both the input nodes and the selected nodes are represented by heterogeneous IDs, and the formed edges are of the input type edge_type. Note that negative refers to false negatives, which means the edge could be present or not present in the graph.
 Return type
Tuple[Tensor, Tensor]

sample_negative_edges_uniform_2
(edge_type, node_pairs, negative_ratio)[source]¶ Sample negative edges by randomly choosing negative sourcedestination edges according to a uniform distribution. For each edge
(u, v)
, it is supposed to generate negative_ratio pairs of negative edges(u, v')
, wherev'
is chosen uniformly from all the nodes in the graph.u
is exactly same as the corresponding positive edges. It returns positive edges concatenated with negative edges. In negative edges, negative sources are constructed from the corresponding positive edges. Parameters
edge_type (str) – The type of edges in the provided node_pairs. Any negative edges sampled will also have the same type. If set to None, it will be considered as a homogeneous graph.
node_pairs (torch.Tensor) – A 2D tensors that represent the N pairs of positive edges in sourcedestination format, with ‘positive’ indicating that these edges are present in the graph. It’s important to note that within the context of a heterogeneous graph, the ids in these tensors signify heterogeneous ids.
negative_ratio (int) – The ratio of the number of negative samples to positive samples.
 Returns
A 2D tensors represents the N pairs of positive and negative sourcedestination node pairs. In the context of a heterogeneous graph, both the input nodes and the selected nodes are represented by heterogeneous IDs, and the formed edges are of the input type edge_type. Note that negative refers to false negatives, which means the edge could be present or not present in the graph.
 Return type
torch.Tensor

sample_neighbors
(nodes: Union[torch.Tensor, Dict[str, torch.Tensor]], fanouts: torch.Tensor, replace: bool = False, probs_name: Optional[str] = None) → dgl.graphbolt.impl.sampled_subgraph_impl.SampledSubgraphImpl[source]¶ Sample neighboring edges of the given nodes and return the induced subgraph.
 Parameters
nodes (torch.Tensor or Dict[str, torch.Tensor]) –
 IDs of the given seed nodes.
If nodes is a tensor: It means the graph is homogeneous graph, and ids inside are homogeneous ids.
If nodes is a dictionary: The keys should be node type and ids inside are heterogeneous ids.
fanouts (torch.Tensor) –
The number of edges to be sampled for each node with or without considering edge types.
When the length is 1, it indicates that the fanout applies to all neighbors of the node as a collective, regardless of the edge type.
Otherwise, the length should equal to the number of edge types, and each fanout value corresponds to a specific edge type of the nodes.
 The value of each fanout should be >= 0 or = 1.
When the value is 1, all neighbors (with nonzero probability, if weighted) will be sampled once regardless of replacement. It is equivalent to selecting all neighbors with nonzero probability when the fanout is >= the number of neighbors (and replace is set to false).
When the value is a nonnegative integer, it serves as a minimum threshold for selecting neighbors.
replace (bool) – Boolean indicating whether the sample is preformed with or without replacement. If True, a value can be selected multiple times. Otherwise, each value can be selected only once.
probs_name (str, optional) – An optional string specifying the name of an edge attribute used. This attribute tensor should contain (unnormalized) probabilities corresponding to each neighboring edge of a node. It must be a 1D floatingpoint or boolean tensor, with the number of elements equalling the total number of edges.
 Returns
The sampled subgraph.
 Return type
Examples
>>> import dgl.graphbolt as gb >>> import torch >>> ntypes = {"n1": 0, "n2": 1} >>> etypes = {"n1:e1:n2": 0, "n2:e2:n1": 1} >>> indptr = torch.LongTensor([0, 2, 4, 6, 7, 9]) >>> indices = torch.LongTensor([2, 4, 2, 3, 0, 1, 1, 0, 1]) >>> node_type_offset = torch.LongTensor([0, 2, 5]) >>> type_per_edge = torch.LongTensor([1, 1, 1, 1, 0, 0, 0, 0, 0]) >>> graph = gb.fused_csc_sampling_graph(indptr, indices, ... node_type_offset=node_type_offset, ... type_per_edge=type_per_edge, ... node_type_to_id=ntypes, ... edge_type_to_id=etypes) >>> nodes = {'n1': torch.LongTensor([0]), 'n2': torch.LongTensor([0])} >>> fanouts = torch.tensor([1, 1]) >>> subgraph = graph.sample_neighbors(nodes, fanouts) >>> print(subgraph.sampled_csc) {'n1:e1:n2': CSCFormatBase(indptr=tensor([0, 1]), indices=tensor([0]), ), 'n2:e2:n1': CSCFormatBase(indptr=tensor([0, 1]), indices=tensor([2]), )}

temporal_sample_neighbors
(nodes: Union[torch.Tensor, Dict[str, torch.Tensor]], input_nodes_timestamp: Union[torch.Tensor, Dict[str, torch.Tensor]], fanouts: torch.Tensor, replace: bool = False, probs_name: Optional[str] = None, node_timestamp_attr_name: Optional[str] = None, edge_timestamp_attr_name: Optional[str] = None) → torch.ScriptObject[source]¶ Temporally Sample neighboring edges of the given nodes and return the induced subgraph.
If node_timestamp_attr_name or edge_timestamp_attr_name is given, the sampled neighbor or edge of an input node must have a timestamp that is smaller than that of the input node.
 Parameters
nodes (torch.Tensor) – IDs of the given seed nodes.
input_nodes_timestamp (torch.Tensor) – Timestamps of the given seed nodes.
fanouts (torch.Tensor) –
The number of edges to be sampled for each node with or without considering edge types.
When the length is 1, it indicates that the fanout applies to all neighbors of the node as a collective, regardless of the edge type.
Otherwise, the length should equal to the number of edge types, and each fanout value corresponds to a specific edge type of the nodes.
 The value of each fanout should be >= 0 or = 1.
When the value is 1, all neighbors (with nonzero probability, if weighted) will be sampled once regardless of replacement. It is equivalent to selecting all neighbors with nonzero probability when the fanout is >= the number of neighbors (and replace is set to false).
When the value is a nonnegative integer, it serves as a minimum threshold for selecting neighbors.
replace (bool) – Boolean indicating whether the sample is preformed with or without replacement. If True, a value can be selected multiple times. Otherwise, each value can be selected only once.
probs_name (str, optional) – An optional string specifying the name of an edge attribute. This attribute tensor should contain (unnormalized) probabilities corresponding to each neighboring edge of a node. It must be a 1D floatingpoint or boolean tensor, with the number of elements equalling the total number of edges.
node_timestamp_attr_name (str, optional) – An optional string specifying the name of an node attribute.
edge_timestamp_attr_name (str, optional) – An optional string specifying the name of an edge attribute.
 Returns
The sampled subgraph.
 Return type

property
csc_indptr
¶ Returns the indices pointer in the CSC graph.
 Returns
The indices pointer in the CSC graph. An integer tensor with shape (total_num_nodes+1,).
 Return type
torch.tensor

property
edge_attributes
¶ Returns the edge attributes dictionary.

property
edge_type_to_id
¶ Returns the edge type to id dictionary if present.

property
indices
¶ Returns the indices in the CSC graph.
 Returns
The indices in the CSC graph. An integer tensor with shape (total_num_edges,).
 Return type
torch.tensor
Notes
It is assumed that edges of each node are already sorted by edge type ids.

property
node_attributes
¶ Returns the node attributes dictionary.

property
node_type_offset
¶ Returns the node type offset tensor if present. Do not modify the returned tensor in place.
 Returns
If present, returns a 1D integer tensor of shape (num_node_types + 1,). The tensor is in ascending order as nodes of the same type have continuous IDs, and larger node IDs are paired with larger node type IDs. The first value is 0 and last value is the number of nodes. And nodes with IDs between node_type_offset_[i]~node_type_offset_[i+1] are of type id ‘i’.
 Return type
torch.Tensor or None

property
node_type_to_id
¶ Returns the node type to id dictionary if present.

property
num_edges
¶ The number of edges in the graph.  If the graph is homogenous, returns an integer.  If the graph is heterogenous, returns a dictionary.
 Returns
The number of edges. Integer indicates the total edges number of a homogenous graph; dict indicates edges number per edge types of a heterogenous graph.
 Return type
Examples
>>> import dgl.graphbolt as gb, torch >>> total_num_nodes = 5 >>> total_num_edges = 12 >>> ntypes = {"N0": 0, "N1": 1} >>> etypes = {"N0:R0:N0": 0, "N0:R1:N1": 1, ... "N1:R2:N0": 2, "N1:R3:N1": 3} >>> indptr = torch.LongTensor([0, 3, 5, 7, 9, 12]) >>> indices = torch.LongTensor([0, 1, 4, 2, 3, 0, 1, 1, 2, 0, 3, 4]) >>> node_type_offset = torch.LongTensor([0, 2, 5]) >>> type_per_edge = torch.LongTensor( ... [0, 0, 2, 2, 2, 1, 1, 1, 3, 1, 3, 3]) >>> metadata = gb.GraphMetadata(ntypes, etypes) >>> graph = gb.fused_csc_sampling_graph(indptr, indices, node_type_offset, ... type_per_edge, None, metadata) >>> print(graph.num_edges) {'N0:R0:N0': 2, 'N0:R1:N1': 1, 'N1:R2:N0': 2, 'N1:R3:N1': 3}

property
num_nodes
¶ The number of nodes in the graph.  If the graph is homogenous, returns an integer.  If the graph is heterogenous, returns a dictionary.
 Returns
The number of nodes. Integer indicates the total nodes number of a homogenous graph; dict indicates nodes number per node types of a heterogenous graph.
 Return type
Examples
>>> import dgl.graphbolt as gb, torch >>> total_num_nodes = 5 >>> total_num_edges = 12 >>> ntypes = {"N0": 0, "N1": 1} >>> etypes = {"N0:R0:N0": 0, "N0:R1:N1": 1, ... "N1:R2:N0": 2, "N1:R3:N1": 3} >>> indptr = torch.LongTensor([0, 3, 5, 7, 9, 12]) >>> indices = torch.LongTensor([0, 1, 4, 2, 3, 0, 1, 1, 2, 0, 3, 4]) >>> node_type_offset = torch.LongTensor([0, 2, 5]) >>> type_per_edge = torch.LongTensor( ... [0, 0, 2, 2, 2, 1, 1, 1, 3, 1, 3, 3]) >>> graph = gb.fused_csc_sampling_graph(indptr, indices, ... node_type_offset=node_type_offset, ... type_per_edge=type_per_edge, ... node_type_to_id=ntypes, ... edge_type_to_id=etypes) >>> print(graph.num_nodes) {'N0': 2, 'N1': 3}

property
total_num_edges
¶ Returns the number of edges in the graph.
 Returns
The number of edges in the graph.
 Return type

property
total_num_nodes
¶ Returns the number of nodes in the graph.
 Returns
The number of rows in the dense format.
 Return type