Graph utils

torchmil.utils.degree(index, edge_weight=None, n_nodes=None)

Compute the degree of the adjacency matrix. Assumes that the graph is undirected.

Parameters:

• index (ndarray) –

  Edge index of the adjacency matrix, shape (2, n_edges).

• edge_weight (ndarray, default: None ) –

  Edge weight of the adjacency matrix, shape (n_edges,).

• n_nodes (int, default: None ) –

  Number of nodes in the graph.

Returns:

• degree ( ndarray ) –

  Degree of the adjacency matrix.

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torchmil.utils.add_self_loops(edge_index, edge_weight=None, n_nodes=None)

Add self-loops to the adjacency matrix.

Parameters:

• edge_index (ndarray) –

  Edge index of the adjacency matrix, shape (2, n_edges).

• edge_weight (ndarray, default: None ) –

  Edge weight of the adjacency matrix, shape (n_edges,).

• n_nodes (int, default: None ) –

  Number of nodes in the graph.

Returns:

• new_edge_index ( ndarray ) –

  Edge index of the adjacency matrix with self-loops.

• new_edge_weight ( ndarray ) –

  Edge weight of the adjacency matrix with self-loops.

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torchmil.utils.normalize_adj(edge_index, edge_weight=None, n_nodes=None)

Normalize the adjacency matrix.

Parameters:

• edge_index (ndarray) –

  Edge index of the adjacency matrix.

• edge_weight (ndarray, default: None ) –

  Edge weight of the adjacency matrix.

• n_nodes (int, default: None ) –

  Number of nodes in the graph.

Returns:

• edge_weight ( ndarray ) –

  Edge weight of the normalized adjacency matrix.

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torchmil.utils.build_adj(coords, feat=None, dist_thr=1.0, add_self_loops=False)

Build the adjacency matrix for a general graph given the coordinates and features of the nodes.

Parameters:

• coords (ndarray) –

  Coordinates of the nodes.

• feat (ndarray, default: None ) –

  Features of the nodes, used to compute the edge weights. If None, the adjacency matrix is binary.

• dist_thr (float, default: 1.0 ) –

  Distance threshold to consider two nodes as neighbors. Default is 1.0.

• add_self_loops (bool, default: False ) –

  Whether to add self-loops.

Returns:

• edge_index ( ndarray ) –

  Edge index of the adjacency matrix.

• edge_weight ( ndarray ) –

  Edge weight of the adjacency matrix