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# CSC 4760/6760 Big Data Programming Assignment 2 solution

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1. (100 points) (PageRank)
Dataset:
The toy dataset is the following graph. The PageRank values are already known. We can use it to
Figure 1: A toy graph for computing PageRank. The number on the edge represents the
transition probability from one node to another.
The PageRank values are given in the following table (given that the decay factor ππ = 0.85):
Nodes PageRank Values
1 0.1556
2 0.1622
3 0.2312
4 0.2955
5 0.1556
PageRank:
Compute the PageRank value of each node in the graph. Please refer to the slides for more details
about the PageRank method. The key PageRank equation is as follows.
π«π« = ππππβ€π«π« + (1 β ππ)ππ/ππ
where π«π« represents the ππ Γ 1 PageRank vector with each element π«π«ππ representing the PageRank
value of node ππ, ππ represents the number of nodes in the graph, ππ represents the ππ Γ ππ transition
probability matrix with each element ππππ,ππ = ππππ,ππ = 1
ππππ
representing the transition probability from
node ππ to node ππ, ππππ represents the degree of node ππ, ππβ€ represents the transpose of ππ, ππ β (0,1)
represents a decay factor, ππ represents a ππ Γ 1 vector of all 1βs, and ππ represents the number of
nodes in the graph.
Please see the slides for more details.
In this assignment, we set the decay factor ππ = 0.85 and set the number of iterations to 30.
Implementation:
Design and implement a MapReduce program to compute the PageRank values.
You are encouraged to implement the PageRank algorithm from scratch without using the
provided βPageRankIncomplete.javaβ file.
The provided βPageRankIncomplete.javaβ file is incomplete. It will help you start programming
with Hadoop. You need to understand the existing code and basic structure in order to complete
the file.
Example command:
hadoop jar PageRank.jar file:///home/rob/pagerank/01InitialPRValues.txt
Report:
Please write a report illustrating your experiments. You need to explain your basic idea about how
to design the computing algorithm. You may add comments to the source code such that the
source code can be read and understood by the graders.
In the report, you should include the answers to the following questions.
1) Explanation of the source code:
1.1) How is the Mapper function defined? Which kind of intermediate results are generated?
1.2) How is the Reducer function defined? How do you aggregate the intermediate results and
get the final outputs?
1.3) Do you use a Combiner function? Why or why not?
2) Experimental Results
2.1) Screenshots of the key steps. For example, the screenshot for the outputs in the terminal
when you run βHadoop jar YourJarFileβ command. It will demonstrate that your program has no
bug.
2.2) Explain your results. Does your implementation give the exact PageRank values? How large
are the errors?
Submission Materials: