easygraph.functions.graph_embedding.sdne module#
- class easygraph.functions.graph_embedding.sdne.Dataload(*args: Any, **kwargs: Any)[source]#
Bases:
Dataset
- class easygraph.functions.graph_embedding.sdne.SDNE(*args: Any, **kwargs: Any)[source]#
Bases:
Module
Graph embedding via SDNE.
graph : easygraph.Graph or easygraph.DiGraph
node: Size of nodes
nhid0, nhid1: Two dimensions of two hiddenlayers, default: 128, 64
dropout: One parameter for regularization, default: 0.025
alpha, beta: Twe parameters graph=g: : easygraph.Graph or easygraph.DiGraph
Examples
>>> import easygraph as eg >>> model = eg.SDNE(graph=g, node_size= len(g.nodes), nhid0=128, nhid1=64, dropout=0.025, alpha=2e-2, beta=10) >>> emb = model.train(model, epochs, lr, bs, step_size, gamma, nu1, nu2, device, output)
epochs, “–epochs”, default=400, type=int, help=”The training epochs of SDNE”
alpha, “–alpha”, default=2e-2, type=float, help=”alhpa is a hyperparameter in SDNE”
beta, “–beta”, default=10.0, type=float, help=”beta is a hyperparameter in SDNE”
lr, “–lr”, default=0.006, type=float, help=”learning rate”
bs, “–bs”, default=100, type=int, help=”batch size of SDNE”
step_size, “–step_size”, default=10, type=int, help=”The step size for lr”
gamma, # “–gamma”, default=0.9, type=int, help=”The gamma for lr”
step_size, “–step_size”, default=10, type=int, help=”The step size for lr”
nu1, # “–nu1”, default=1e-5, type=float, help=”nu1 is a hyperparameter in SDNE”
nu2, “–nu2”, default=1e-4, type=float, help=”nu2 is a hyperparameter in SDNE”
device, “– device = torch.device(“cuda:0” if torch.cuda.is_available() else “cpu”) “
output “–output”, default=”node.emb”, help=”Output representation file”