Function load_data

examples/sparse/hypergraphatt.py:92–112

()

def load_data():
    dataset = CoraGraphDataset()

    graph = dataset[0]
    # The paper created a hypergraph from the original graph. For each node in
    # the original graph, a hyperedge in the hypergraph is created to connect
    # its neighbors and itself. In this case, the incidence matrix of the
    # hypergraph is the same as the adjacency matrix of the original graph (with
    # self-loops).
    # We follow the paper and assume that the rows of the incidence matrix
    # are for nodes and the columns are for edges.
    indices = torch.stack(graph.edges())
    H = dglsp.spmatrix(indices)
    H = H + dglsp.identity(H.shape)

    X = graph.ndata["feat"]
    Y = graph.ndata["label"]
    train_mask = graph.ndata["train_mask"]
    val_mask = graph.ndata["val_mask"]
    test_mask = graph.ndata["test_mask"]
    return H, X, Y, dataset.num_classes, train_mask, val_mask, test_mask


def main(args):