Github user luyi0619 commented on the pull request:
https://github.com/apache/spark/pull/1128#issuecomment-47226594
Hi ankurdave,
In the graph you given(1-->2), I think your pagerank implementation will
give the same results.
when the initalvalue is 0.0
a b
0 0
0.15 0.15
0.15 0.2775
when the initalvalue is 1.0
1 1
0.15 1
0.15 0.2775
Actually, there is another problem maybe we should handle if there is
0-outdegree vertices in the graph(e.g. 2). One solution is to distribute their
pagerank values to all the vertices uniformly, but the initialvalue should be
one.
For example,
a b
1 1
0.575 1.425
0.755625 1.24438
0.678859 1.32114
0.711485 1.28852
-----------------------
Omit some iterations.
-----------------------
0.701756 1.29824
0.701754 1.29825
0.701755 1.29825
In my understanding, if there is no 0-outdegree vertices in a graph, the
sum of pagerank values from all vertices should remain the same. However, in
the graphX implementation, this is not true.
In a word, I still think the initialvalue should be 1.0, you could also
refer to graphlab's implementation.
52th line of
https://github.com/graphlab-code/graphlab/blob/master/toolkits/graph_analytics/pagerank.cpp
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