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2012-12-11

Practice final solution for Problem 10.

Originally Posted By: ranjith

10. Ignoring dangling nodes and the random jump factor give the mapper and reducer for a Map Reduce implementation of page rank.

Ans.

MAP REDUCE IMPLEMENTATION OF PAGE RANK
Let epsilon be the constant we used to decide if we stop.
Compute initial current_r
do
{
store in distributed file system [DFS] current_r as old_r.
Do map reduce job to compute A* current_r
where A is the normalized adjacency matrix
store result in DFS as current_r
Do map reduce job to compute len = ||current_r - old_r || /*A seperate map reduce job should be created to calculate (current_r_old_r) */ᵋ
} while (len>epsilon)
output current_r as page rank

class MAPPER [to calculate A*current_r]
method MAP(nid n , Node N)
p Pass Page rank to neighbors

class REDUCER
method REDUCE ( nid m , [p1, p2,....])
m ranjith — Tue Dec 11, 2012 11:43 am <hr>

'''Originally Posted By: ranjith''' 10. Ignoring dangling nodes and the random jump factor give the mapper and reducer for a Map Reduce implementation of page rank. Ans. MAP REDUCE IMPLEMENTATION OF PAGE RANK Let epsilon be the constant we used to decide if we stop. Compute initial current_r do { store in distributed file system [DFS] current_r as old_r. Do map reduce job to compute A* current_r where A is the normalized adjacency matrix store result in DFS as current_r Do map reduce job to compute len = ||current_r - old_r || /*A seperate map reduce job should be created to calculate (current_r_old_r) */ᵋ } while (len>epsilon) output current_r as page rank class MAPPER [to calculate A*current_r] method MAP(nid n , Node N) p Pass Page rank to neighbors class REDUCER method REDUCE ( nid m , [p1, p2,....]) m ranjith — Tue Dec 11, 2012 11:43 am <hr>