chore: import upstream snapshot with attribution

examples/sparse/pagerank.py

@@ -0,0 +1,31 @@
+import dgl.sparse as dglsp
+import networkx as nx
+import torch
+
+N = 100
+DAMP = 0.85
+K = 10
+
+
+def pagerank(A):
+    D = A.sum(0)
+    V = torch.ones(N) / N
+    for _ in range(K):
+        ########################################################################
+        # (HIGHLIGHT) Take the advantage of DGL sparse APIs to calculate the
+        # page rank.
+        ########################################################################
+        V = (1 - DAMP) / N + DAMP * A @ (V / D)
+    return V
+
+
+if __name__ == "__main__":
+    g = nx.erdos_renyi_graph(N, 0.05, seed=10086)
+
+    # Create the adjacency matrix of graph.
+    edges = list(g.to_directed().edges())
+    indices = torch.tensor(edges).transpose(0, 1)
+    A = dglsp.spmatrix(indices, shape=(N, N))
+
+    V = pagerank(A)
+    print(V)