gammagl.utils

gammagl.utils.calc_A_norm_hat
gammagl.utils.calc_gcn_norm	calculate GCN Normalization.
gammagl.utils.segment_softmax	segment softmax function.
gammagl.utils.sort_edge_index	Row-wise sorts edge_index.
gammagl.utils.coalesce	Row-wise sorts edge_index and removes its duplicated entries.
gammagl.utils.to_undirected	Converts the graph given by edge_index to an undirected graph such that \((j,i) \in \mathcal{E}\) for every edge \((i,j) \in \mathcal{E}\).
gammagl.utils.is_undirected	Returns True if the graph given by edge_index is undirected.
gammagl.utils.degree	Computes the (unweighted) degree of a given one-dimensional index tensor.
gammagl.utils.add_self_loops	Adds a self-loop \((i,i) \in \mathcal{E}\) to every node \(i \in \mathcal{V}\) in the graph given by edge_index.
gammagl.utils.remove_self_loops	Removes every self-loop in the graph given by edge_index, so that \((i,i) \not\in \mathcal{E}\) for every \(i \in \mathcal{V}\).
gammagl.utils.mask_to_index	Converts a mask to an index representation.
gammagl.utils.index_to_mask	Converts indices to a mask representation.
gammagl.utils.set_device
gammagl.utils.to_dense_batch	Given a sparse batch of node features \(\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}\) (with \(N_i\) indicating the number of nodes in graph \(i\)), creates a dense node feature tensor \(\mathbf{X} \in \mathbb{R}^{B \times N_{\max} \times F}\) (with \(N_{\max} = \max_i^B N_i\)).
gammagl.utils.k_hop_subgraph	Computes the induced subgraph of edge_index around all nodes in node_idx reachable within \(k\) hops.
gammagl.utils.negative_sampling	Samples random negative edges of a graph given by edge_index.
gammagl.utils.to_scipy_sparse_matrix	Converts a graph given by edge indices and edge attributes to a scipy sparse matrix.
gammagl.utils.read_embeddings	read pre trained and learned node embeddings
gammagl.utils.homophily	The homophily of a graph characterizes how likely nodes with the same label are near each other in a graph.