class pagerank_multihop_graph_interdependence(graph_interdependence):
def __init__(self, c: float = 0.15, name: str = 'pagerank_multihop_graph_interdependence', *args, **kwargs):
super().__init__(name=name, *args, **kwargs)
self.c = c
def calculate_A(self, x: torch.Tensor = None, w: torch.nn.Parameter = None, device: str = 'cpu', *args, **kwargs):
if not self.require_data and not self.require_parameters and self.A is not None:
return self.A
else:
adj, mappings = self.graph.to_matrix(normalization=self.normalization, normalization_mode=self.normalization_mode, device=device)
self.node_id_index_map = mappings['node_id_index_map']
self.node_index_id_map = mappings['node_index_id_map']
A = self.c * torch.inverse((torch.eye(adj.shape[0], device=device) - (1.0 - self.c) * adj))
A = self.post_process(x=A, device=device)
if self.interdependence_type in ['column', 'right', 'attribute', 'attribute_interdependence']:
assert A.shape == (self.m, self.calculate_m_prime())
elif self.interdependence_type in ['row', 'left', 'instance', 'instance_interdependence']:
assert A.shape == (self.b, self.calculate_b_prime())
if not self.require_data and not self.require_parameters and self.A is None:
self.A = A
return A