wehub-resource-sync
32 lines
792 B
Python
32 lines
792 B
Python
import dgl.sparse as dglsp
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import networkx as nx
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import torch
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N = 100
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DAMP = 0.85
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K = 10
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def pagerank(A):
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D = A.sum(0)
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V = torch.ones(N) / N
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for _ in range(K):
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########################################################################
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# (HIGHLIGHT) Take the advantage of DGL sparse APIs to calculate the
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# page rank.
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########################################################################
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V = (1 - DAMP) / N + DAMP * A @ (V / D)
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return V
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if __name__ == "__main__":
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g = nx.erdos_renyi_graph(N, 0.05, seed=10086)
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# Create the adjacency matrix of graph.
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edges = list(g.to_directed().edges())
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indices = torch.tensor(edges).transpose(0, 1)
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A = dglsp.spmatrix(indices, shape=(N, N))
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V = pagerank(A)
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print(V)
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