torch-ppr
torch-ppr is a package to calculate (personalized) page ranks with PyTorch.
As a simple example, consider this simple graph with five nodes.
Its edge list is given as
edge_index = torch.as_tensor(data=[(0, 1), (1, 2), (1, 3), (2, 4)]).t()
We can use
>>> page_rank(edge_index=edge_index)
tensor([0.1269, 0.3694, 0.2486, 0.1269, 0.1281])
to calculate the page rank, i.e., a measure of global importance. We notice that the central node receives the largest importance score, while all other nodes have equal importance.
We can also calculate personalized page rank which measures importance from the perspective of a single node. For instance, for node 2, we have
>>> personalized_page_rank(edge_index=edge_index, indices=[2])
tensor([[0.1103, 0.3484, 0.2922, 0.1103, 0.1388]])
By the virtue of using PyTorch, the code seamlessly works on GPUs, too, and supports auto-grad differentiation. Moreover, the calculation of personalized page rank supports automatic batch size optimization via torch_max_mem.