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[Optimization] Use scipy's eigs instead of numpy in lap_pe #5855

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2 changes: 1 addition & 1 deletion python/dgl/transforms/functional.py
Original file line number Diff line number Diff line change
Expand Up @@ -3671,8 +3671,8 @@
) # D^-1/2
L = sparse.eye(g.num_nodes()) - N * A * N

# select eigenvectors with smaller eigenvalues O(n + klogk)

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EigVal, EigVec = np.linalg.eig(L.toarray())
EigVal, EigVec = scipy.sparse.linalg.eigs(L, k=pos_enc_dim+1, which='SR', tol=1e-2)
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max_freqs = min(n - 1, k)
kpartition_indices = np.argpartition(EigVal, max_freqs)[: max_freqs + 1]
topk_eigvals = EigVal[kpartition_indices]
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