Big Data Programming Assignment 2

CSC 4760/6760 Big Data Programming
Assignment 2

1. (100 points) (PageRank)
Dataset:
The toy dataset is the following graph. The PageRank values are already known. We can use it to
check your program.
Figure 1: A toy graphfor 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 𝑐𝑐 = 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.
𝐫𝐫 = 𝑐𝑐𝐏𝐏⊤𝐫𝐫 + (1 − 𝑐𝑐)𝟏𝟏/𝑛𝑛
where 𝐫𝐫 represents the 𝑛𝑛 × 1 PageRank vector with each element 𝐫𝐫𝑖𝑖 representing the PageRank
value of node 𝑖𝑖, 𝑛𝑛 represents the number of nodes in the graph, 𝐏𝐏 represents the 𝑛𝑛 × 𝑛𝑛 transition
probability matrix with each element 𝐏𝐏𝑖𝑖,𝑗𝑗 = 𝑝𝑝𝑖𝑖,𝑗𝑗 = 1
𝑑𝑑𝑖𝑖
representing the transition probability from
node 𝑖𝑖 to node 𝑗𝑗, 𝑑𝑑𝑖𝑖 represents the degree of node 𝑖𝑖, 𝐏𝐏⊤ representsthe transpose of 𝐏𝐏, 𝑐𝑐 ∈ (0,1)
represents a decay factor, 𝟏𝟏 represents a 𝑛𝑛 × 1 vector of all 1’s, and 𝑛𝑛 represents the number of
nodes in the graph.
Please see the slides for more details.
In this assignment, we set the decay factor 𝑐𝑐 = 0.85and 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 “PageRankIncomplete.java” file.
The provided “PageRankIncomplete.java” 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
file:///home/rob/pagerank/02AdjacencyList.txt file:///home/rob/pagerank/output 30
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 “Hadoop jar YourJarFile” 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:
a) Your report
b) Source code (.java file) and the .jar file for the Hadoop
c) The output file of your program.