dmlc--dgl
peizhou001
25 行
594 B
Python
25 行
594 B
Python
import networkx as nx
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import torch
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import dgl
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import dgl.function as fn
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N = 100
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g = nx.erdos_renyi_graph(N, 0.05)
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g = dgl.DGLGraph(g)
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DAMP = 0.85
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K = 10
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def compute_pagerank(g):
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g.ndata['pv'] = torch.ones(N) / N
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degrees = g.out_degrees(g.nodes()).type(torch.float32)
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for k in range(K):
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g.ndata['pv'] = g.ndata['pv'] / degrees
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g.update_all(message_func=fn.copy_u(u='pv', out='m'),
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reduce_func=fn.sum(msg='m', out='pv'))
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g.ndata['pv'] = (1 - DAMP) / N + DAMP * g.ndata['pv']
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return g.ndata['pv']
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pv = compute_pagerank(g)
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print(pv)
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