page rank algorithm
Google's PageRank ( 100 points) Implement the Google PageRank algorithm as explained below. The input for this problem would be a graph represented through an adjacency list representation. The command-line interface that would be used is as follows. The first two of the three parameters hold integer values; the last parameter is a filename.
% ./pagerank iterations initialvalue filename % java pagerank iterations initialvalue filename
The PageRank of vertex A depends on the PageRanks of vertices T1,.....,Tm of vertices incident to A. The value of d is 0.85. C(Ti)) is the out-degree of vertex Ti. When we compute ranks iteratively we use the previous iteration values to update the current iteration values!
PR(A) = (1 - d)/n + d (PR(T1)/C(T1) + ... + PR(Tm)/C(Tm))
Thus PR(A) is the value to be obtained in the current iteration, but all PR (Ti) values are from the previous iteration. This is called a synchronized update. Also note that for this assignment the user may control the number of iterations. Another approach is that the iterations terminate when a given error rate is achieved. This means that for every vertex the corresponding PageRank values between two successive iterations is LESS than a given error bound say 10 raise to -4.Thus instead of specifying iterations one may specify an error bound (or rate) and the algorithm checks how many iterations are needed to achieve this error rate, if possible. The rst parameter iterations runs the algorithms for that number of iterations if iterations is a non-zero positive integer (natural integer). A zero (0) has a special meaning; you need to run as many iterations k as needed. You stop at iteration k if each one of pageranks between iteration k - 1 and k is smaller than a fixed error 10 raise to -4.
The second parameter initialvalue shows the initial values for the ranks. If it is 0 all ranks are initialized to 0, if it is 1 they are initialized to 1. If it is -1 they are initialized to 1/N, where N is the number of web-pages (vertices of the graph). If it is -2 they are initialized to 1/root of N, where N is the number of web-pages (vertices of the graph). (In order to determine N you need to construct the graph described in file filename first.)
The third parameter filename describes the input (directed) graph and it has the following form. The first line contains two numbers: the number of vertices (in the example below, this is equal to four and is denoted by the first four) and the number of edges that follow on separate lines (the second four in the example). In each line an edge (i, j) is represented by i j. Thus our graph has (directed) edges (0, 2); (0, 3); (1, 0); (2, 1).
Pageranks are printed to six decimal digits. If the graph has N greater than 10, then the values for iterations, initialvalue revert to 0 and -1 respectively. In such a case the pageranks at the stopping iteration are ONLY shown, one per line.
The graph below will be referred to as samplegraph.txt
4 4
0 2
0 3
1 0
2 1
The following invocations relate to samplegraph.txt, with a fixed number of iterations and the xed error rate that determines how many iterations will run. Your code should compute for this graph the same rank values (intermediate and final). A sample of the output for the case of N > 10 is shown (output truncated to first 4 lines of it).
% ./pagerank 15 -1 samplegraph.txt
Base : 0 :P[ 0]=0.250000 P[ 1]=0.250000 P[ 2]=0.250000 P[ 3]=0.250000
Iter : 1 :P[ 0]=0.250000 P[ 1]=0.250000 P[ 2]=0.143750 P[ 3]=0.143750
Iter : 2 :P[ 0]=0.250000 P[ 1]=0.159687 P[ 2]=0.143750 P[ 3]=0.143750
Iter : 3 :P[ 0]=0.173234 P[ 1]=0.159687 P[ 2]=0.143750 P[ 3]=0.143750
Iter : 4 :P[ 0]=0.173234 P[ 1]=0.159687 P[ 2]=0.111125 P[ 3]=0.111125
Iter : 5 :P[ 0]=0.173234 P[ 1]=0.131956 P[ 2]=0.111125 P[ 3]=0.111125
Iter : 6 :P[ 0]=0.149663 P[ 1]=0.131956 P[ 2]=0.111125 P[ 3]=0.111125
Iter : 7 :P[ 0]=0.149663 P[ 1]=0.131956 P[ 2]=0.101107 P[ 3]=0.101107
Iter : 8 :P[ 0]=0.149663 P[ 1]=0.123441 P[ 2]=0.101107 P[ 3]=0.101107
Iter : 9 :P[ 0]=0.142425 P[ 1]=0.123441 P[ 2]=0.101107 P[ 3]=0.101107
Iter : 10 :P[ 0]=0.142425 P[ 1]=0.123441 P[ 2]=0.098030 P[ 3]=0.098030
Iter : 11 :P[ 0]=0.142425 P[ 1]=0.120826 P[ 2]=0.098030 P[ 3]=0.098030
Iter : 12 :P[ 0]=0.140202 P[ 1]=0.120826 P[ 2]=0.098030 P[ 3]=0.098030
Iter : 13 :P[ 0]=0.140202 P[ 1]=0.120826 P[ 2]=0.097086 P[ 3]=0.097086
Iter : 14 :P[ 0]=0.140202 P[ 1]=0.120023 P[ 2]=0.097086 P[ 3]=0.097086
Iter : 15 :P[ 0]=0.139520 P[ 1]=0.120023 P[ 2]=0.097086 P[ 3]=0.097086
% ./pagerank 0 -1 samplegraph.txt
Base : 0 :P[ 0]=0.250000 P[ 1]=0.250000 P[ 2]=0.250000 P[ 3]=0.250000
Iter : 1 :P[ 0]=0.250000 P[ 1]=0.250000 P[ 2]=0.143750 P[ 3]=0.143750
Iter : 2 :P[ 0]=0.250000 P[ 1]=0.159687 P[ 2]=0.143750 P[ 3]=0.143750
Iter : 3 :P[ 0]=0.173234 P[ 1]=0.159687 P[ 2]=0.143750 P[ 3]=0.143750
Iter : 4 :P[ 0]=0.173234 P[ 1]=0.159687 P[ 2]=0.111125 P[ 3]=0.111125
Iter : 5 :P[ 0]=0.173234 P[ 1]=0.131956 P[ 2]=0.111125 P[ 3]=0.111125
Iter : 6 :P[ 0]=0.149663 P[ 1]=0.131956 P[ 2]=0.111125 P[ 3]=0.111125
Iter : 7 :P[ 0]=0.149663 P[ 1]=0.131956 P[ 2]=0.101107 P[ 3]=0.101107
Iter : 8 :P[ 0]=0.149663 P[ 1]=0.123441 P[ 2]=0.101107 P[ 3]=0.101107
Iter : 9 :P[ 0]=0.142425 P[ 1]=0.123441 P[ 2]=0.101107 P[ 3]=0.101107
Iter : 10 :P[ 0]=0.142425 P[ 1]=0.123441 P[ 2]=0.098030 P[ 3]=0.098030
Iter : 11 :P[ 0]=0.142425 P[ 1]=0.120826 P[ 2]=0.098030 P[ 3]=0.098030
Iter : 12 :P[ 0]=0.140202 P[ 1]=0.120826 P[ 2]=0.098030 P[ 3]=0.098030
Iter : 13 :P[ 0]=0.140202 P[ 1]=0.120826 P[ 2]=0.097086 P[ 3]=0.097086
Iter : 14 :P[ 0]=0.140202 P[ 1]=0.120023 P[ 2]=0.097086 P[ 3]=0.097086
Iter : 15 :P[ 0]=0.139520 P[ 1]=0.120023 P[ 2]=0.097086 P[ 3]=0.097086
Iter : 16 :P[ 0]=0.139520 P[ 1]=0.120023 P[ 2]=0.096796 P[ 3]=0.096796
Iter : 17 :P[ 0]=0.139520 P[ 1]=0.119776 P[ 2]=0.096796 P[ 3]=0.096796
Iter : 18 :P[ 0]=0.139310 P[ 1]=0.119776 P[ 2]=0.096796 P[ 3]=0.096796
Iter : 19 :P[ 0]=0.139310 P[ 1]=0.119776 P[ 2]=0.096707 P[ 3]=0.096707
% ./pagerank 0 -1 verylargegraph.txt
Iter : 3
P[ 0] = 0.021429
P[ 1] = 0.030536
P[ 2] = 0.027500
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other vertices omitted