jittor_geometric.utils

jittor_geometric.utils.add_remaining_self_loops(edge_index, edge_weight=None, fill_value=1.0, num_nodes=None)

Adds remaining self-loop \((i,i) \in \mathcal{E}\) to every node \(i \in \mathcal{V}\) in the graph given by edge_index. In case the graph is weighted and already contains a few self-loops, only non-existent self-loops will be added with edge weights denoted by fill_value.

Parameters:

• edge_index (Var int32) – The edge indices.

• edge_weight (Var, optional) – One-dimensional edge weights. (default: None)

• fill_value (float, optional) – If edge_weight is not None, will add self-loops with edge weights of fill_value to the graph. (default: 1.)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type:

(Var int32, Var)

jittor_geometric.utils.add_self_loops(edge_index, edge_weight=None, fill_value=1.0, num_nodes=None)

Adds a self-loop \((i,i) \in \mathcal{E}\) to every node \(i \in \mathcal{V}\) in the graph given by edge_index. In case the graph is weighted, self-loops will be added with edge weights denoted by fill_value.

Parameters:

• edge_index (Var int32) – The edge indices.

• edge_weight (Var, optional) – One-dimensional edge weights. (default: None)

• fill_value (float, optional) – If edge_weight is not None, will add self-loops with edge weights of fill_value to the graph. (default: 1.)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type:

(Var int32, Var)

jittor_geometric.utils.coalesce(edge_index, edge_weight=None, num_nodes=None, reduce='sum', is_sorted=False, sort_by_row=True)

jittor_geometric.utils.contains_isolated_nodes(edge_index, num_nodes=None)

Returns True if the graph given by edge_index contains isolated nodes.

Parameters:

• edge_index (Var int32) – The edge indices.

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type:

bool

jittor_geometric.utils.contains_self_loops(edge_index)

Returns True if the graph given by edge_index contains self-loops.

Parameters:

edge_index (Var int32) – The edge indices.

Return type:

bool

jittor_geometric.utils.degree(index, num_nodes=None, dtype=None)

Computes the (unweighted) degree of a given one-dimensional index Var.

jittor_geometric.utils.get_laplacian(edge_index, edge_weight=None, normalization=None, dtype=None, num_nodes=None)

Computes the graph Laplacian of the graph given by edge_index and optional edge_weight.

Parameters:

• edge_index (Var int32) – The edge indices.

• edge_weight (Var, optional) – One-dimensional edge weights. (default: None)

• normalization (str, optional) –

  The normalization scheme for the graph Laplacian (default: None):

  1. None: No normalization \(\mathbf{L} = \mathbf{D} - \mathbf{A}\)

  2. "sym": Symmetric normalization \(\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2}\)

  3. "rw": Random-walk normalization \(\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1} \mathbf{A}\)

• dtype (Var.dtype, optional) – The desired data type of returned Var in case edge_weight=None. (default: None)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

jittor_geometric.utils.induced_graph(edge_index, node_selected, max_nodes)

Generate the node-induced graph of the original graph described by ‘edge_index’. It is expected that max_nodes is larger than, or equal to the real max node index in edge_index, or may cause errors.

Parameters:

• edge_index (jittor.Var) – The edge list describing the whole graph.

• node_selected (jittor.Var) – The node list of the expected induced graph.

• max_nodes (int) – The maximum node index in edge_index.

Return type:

Tuple[Var, Var]

jittor_geometric.utils.is_jittor_sparse_tensor(src)

Returns True if the input src is a jittor.sparse.Tensor (in any sparse layout).

Parameters:

src (Any) – The input object to be checked.

Return type:

bool

jittor_geometric.utils.maybe_num_nodes(edge_index, num_nodes=None)

jittor_geometric.utils.neighbor_sampler(neighbor_list, neighbor_offs, neighbor_nums, source_node, max_nodes=None, max_edges=None)

Samples the neighbor of all the source nodes, in the given graph described by neighbor_list, neighbor_hook, and neighbor_offs.

Parameters:

• neighbor_list (jittor.Var) – An ordered list of neighbors.

• neighbor_offs (jittor.Var) – For each node i, the neighbors of i are neighbor_list[neighbor_offs[i], neighbor_offs[i+1]]. There is neighbor_offs[max_nodes], which should equals to negihbor_list.size(0).

• neighbor_nums (jittor.Var) – For each node i, the number of its neighbors is neighbor_nums[i] == neighbor_offs[i+1] - neighbor_offs[i].For neighbor_nums[i] == 0, should be preprocessed (usually assigned max_edges).

• source_node (jittor,Var) – The source node of sampling.

• max_nodes (int, optional) – The total number of nodes, assert to be (the length of neighbor_offs) - 1 and (the length of neighbor_nums) .

• max_edges (int, optional) – The total number of edges, assert to be the length of neighbor_list.

Return type:

Var

jittor_geometric.utils.one_hot(index, num_classes=None)

Taskes a one-dimensional index var and returns a one-hot encoded representation of it with shape [*, num_classes] that has zeros everywhere except where the index of last dimension matches the corresponding value of the input var, in which case it will be 1.

Parameters:

• index (jittor.Var) – The one-dimensional input var.

• num_classes (int, optional) – The total number of classes. If set to None, the number of classes will be inferred as one greater than the largest class value in the input var. (default: None)

• dtype (jittor.dtype, optional) – The dtype of the output var.

Return type:

Var

jittor_geometric.utils.randomwalk_sampler(neighbor_list, neighbor_offs, neighbor_nums, source_node, walk_length, max_nodes=None, max_edges=None)

Samples the random_walk of all the source nodes with length ‘walk_length’, in the given graph described by neighbor_list, neighbor_hook, and neighbor_offs.

Parameters:

• neighbor_list (jittor.Var) – An ordered list of neighbors.

• neighbor_offs (jittor.Var) – For each node i, the neighbors of i are neighbor_list[neighbor_offs[i], neighbor_offs[i+1]]. There is neighbor_offs[max_nodes], which should equals to negihbor_list.size(0).

• neighbor_nums (jittor.Var) – For each node i, the number of its neighbors is neighbor_nums[i] == neighbor_offs[i+1] - neighbor_offs[i].For neighbor_nums[i] == 0, should be preprocessed (usually assigned max_edges).

• source_node (jittor,Var) – The source node of sampling.

• walk_length (int) – The length of random walk.

• max_nodes (int, optional) – The total number of nodes, assert to be (the length of neighbor_offs) - 1 and (the length of neighbor_nums) .

• max_edges (int, optional) – The total number of edges, assert to be the length of neighbor_list.

Return type:

Var

jittor_geometric.utils.remove_isolated_nodes(edge_index, edge_attr=None, num_nodes=None)

Removes the isolated nodes from the graph given by edge_index with optional edge attributes edge_attr. In addition, returns a mask of shape [num_nodes] to manually filter out isolated node features later on. Self-loops are preserved for non-isolated nodes.

Parameters:

• edge_index (Var int32) – The edge indices.

• edge_attr (Var, optional) – Edge weights or multi-dimensional edge features. (default: None)

• num_nodes (int, optional) – The number of nodes, i.e. max_val + 1 of edge_index. (default: None)

Return type:

(Var int32, Var, Var bool)

jittor_geometric.utils.remove_self_loops(edge_index, edge_attr=None)

Removes every self-loop in the graph given by edge_index, so that \((i,i) \not\in \mathcal{E}\) for every \(i \in \mathcal{V}\).

Parameters:

• edge_index (Var int32) – The edge indices.

• edge_attr (Var, optional) – Edge weights or multi-dimensional edge features. (default: None)

Return type:

(Var int32, Var)

jittor_geometric.utils.scatter(src, index, dim=0, dim_size=None, reduce='sum')

Reduces all values from the src tensor at the indices specified in the index tensor along a given dimension dim. See the documentation of the torch_scatter package for more information.

Parameters:

• src (torch.Var) – The source tensor.

• index (torch.Var) – The index tensor.

• dim (int, optional) – The dimension along which to index. (default: 0)

• dim_size (int, optional) – The size of the output tensor at dimension dim. If set to None, will create a minimal-sized output tensor according to index.max() + 1. (default: None)

• reduce (str, optional) – The reduce operation ("sum", "mean", "mul", "min" or "max"). (default: "sum")

Return type:

Var

jittor_geometric.utils.segregate_self_loops(edge_index, edge_attr=None)

Segregates self-loops from the graph.

Parameters:

• edge_index (Var int32) – The edge indices.

• edge_attr (Var, optional) – Edge weights or multi-dimensional edge features. (default: None)

Return type:

(Var int32, Var, Var int32, Var)

jittor_geometric.utils.unique(inputs, max_value=None)

Sorts the elements of the inputs tensor in ascending order, and then delete the duplicated values. It is expected that inputs is one-dimensional and that it only contains positive integer values. If max_value is given, it can be used by the underlying algorithm for better performance (well this is fake because the index_sort() above cannot be directly called).

Parameters:

• inputs (jittor.Var) – A vector with positive integer values.

• max_value (int, optional) – The maximum value stored inside inputs. This value can be an estimation, but needs to be greater than or equal to the real maximum. (default: None)

Return type:

Var

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