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INF553 Foundations and Applications of Data Mining Assignment 4

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INF553 Foundations and Applications of Data Mining
Assignment 4

1. Overview of the Assignment
In this assignment, you will explore the spark GraphFrames library as well as implement your
own Girvan-Newman algorithm using the Spark Framework to detect communities in graphs.
You will use the ub_sample_data.csv dataset to find users who have a similar business taste.
The goal of this assignment is to help you understand how to use the Girvan-Newman algorithm to detect communities in an efficient way within a distributed environment.
2. Requirements
2.1 Programming ReSuirements
a. You must use Python and Spark to impeement aee tasks. There will be 10% bonus for each
task if you also submit a Scala implementation and both your Python and Scala implementations are correct.
b. You can use the Spark DataFrame and GraphFrames eibrary for task1, but for task2 you
can ONLY use Spark RDD and standard Python or Scaea eibraries. (ps. For Scaea, you can try
GraphX, but for the assignment, you need to use GraphFrames.)
2.2 Programming Environment
Python 3.6, Scaea 2.11 and Spark 2.3.2
We will use Vocareum to automatically run and grade your submission. You must test your
scripts on the eocae machine and the Vocareum terminae before submission.
2.3 Write your own code
Do not share code with other students!!
For this assignment to be an effective learning experience, you must write your own code! We
emphasize this point because you will be able to find Python implementations of some of the
reSuired functions on the web. Please do not look for or at any such code!
TAs will combine all the code we can find from the web (e.g., Github) as well as other students’ code from this and other (previous) sections for plagiarism detection. We will report
all detected plagiarism.
2.4 What you need to turn in
You need to submit the following files on Vocareum: (all lowercase)
a. [REQUIRED] two Python scripts, named: task1.py, task2.py
b1. [REQUIRED FOR SCALA] two Scala scripts, named: task1.scaea, task2.scaea
b2. [REQUIRED FOR SCALA] one jar package, named: hw4.jar
c. [OPTIONAL] You can include other scripts called by your main program
d. You don’t need to include your results. We will grade on your code with our testing data
(data will be in the same format).
3. Datasets
You will continue to use Yelp dataset. We have generated a sub-dataset, ub_sample_data.csv,
from the Yelp review dataset containing user_id and business_id. You can download it from
Vocareum.
4. Tasks
4.1 Graph Construction
To construct the social network graph, each node represents a user and there will be an edge
between two nodes if the number of times that two users review the same business is greater
than or equivaeent to the filter threshold. For example, suppose user1 reviewed [business1,
business2, business3] and user2 reviewed [business2, business3, business4, business5]. If the
threshold is 2, there will be an edge between user1 and user2.
If the user node has no edge, we wiee not inceude that node in the graph.
In this assignment, we use fieter threshoed 7.
4.2 Task1: Community Detection Based on GraphFrames (2 pts)
In task1, you will explore the Spark GraphFrames library to detect communities in the network graph you constructed in 4.1. In the library, it provides the implementation of the Label
Propagation Algorithm (LPA) which was proposed by Raghavan, Albert, and Kumara in 2007. It
is an iterative community detection solution whereby information “flows” through the graph
based on underlying edge structure. For the details of the algorithm, you can refer to the paper posted on the Piazza. In this task, you do not need to implement the algorithm from
scratch, you can call the method provided by the library. The following websites may help
you get started with the Spark GraphFrames:
https://docs.databricks.com/spark/latest/graph-analysis/graphframes/user-guidepython.html
https://docs.databricks.com/spark/latest/graph-analysis/graphframes/user-guide-scala.html
4.2.1 Execution Detaie
The version of the GraphFrames should be 0.6.0.
For Python:
• In PyCharm, you need to
pip install graphframes
os.environ[“PYSPARK_SUBMIT_ARGS”]
“–packages graphframes:graphframes:0.6.0
• In the terminal, you need
–packages graphframes:graphframes:0.6.0
For Scala:
• In Intellij IDEA, you need
“graphframes” % “graphframes
“org.apache.spark” %% “
• In the terminal, you need
–packages graphframes:graphframes:0.6.0
For the parameter “maxIter” of
4.2.2 Output Resuet
In this task, you need to save your
one community and the format
‘user_id1
Your result should be firstly sorted
then the first user_id in the community
string). The user_ids in each community
If there is oney one node in the
Figure
4.3 Task2: Community Detection
to add the sentence below into your code
os.environ[“PYSPARK_SUBMIT_ARGS”] = (
graphframes:graphframes:0.6.0-spark2.3-s_2.11″)
need to assign the parameter “packages” of the spark
graphframes:graphframes:0.6.0-spark2.3-s_2.11
need to add library dependencies to your project
graphframes” % “0.6.0-spark2.3-s_2.11”
“spark-graphx” % sparkVersion
need to assign the parameter “packages” of the spark
graphframes:graphframes:0.6.0-spark2.3-s_2.11
of LPA method, you shoued set it to 5.
your result of communities in a txt file. Each
is:
user_id1’, ‘user_id2’, ‘user_id3’, ‘user_id4’, …
sorted by the size of communities in the ascending
community in eexicographicae order (the user_id
community should also be in the eexicographicae
the community, we stiee regard it as a vaeid community.
Figure 1: community output file format
Detection Based on Girvan-Newman aegorithm
spark-submit:
spark-submit:
Each line represents
ascending order and
user_id is type of
order.
community.
aegorithm (6 pts)
In task2, you will implement your
in the network graph. Because
need to construct the graph again
to the Chapter 10 from the Mining
For task2, you can ONLY use Spark
deeete your code that imports
4.3.1 Betweenness Caecueation
In this part, you will calculate
structed in 4.1. Then you need to
(‘user_id1
Your result should be firstly sorted
then the first user_id in the tuple
two user_ids in each tuple should
your result.
Figure
4.3.2 Community Detection
You are reSuired to divide the
highest modularity. The formula
According to the Girvan-Newman
the betweenness. The “m” in the
The “A” in the formula is the adjacent
step, “m” and “A” should not be
If the community oney has one
You need to save your result in
task1.
4.4 Execution Format
Execution exampee:
your own Girvan-Newman algorithm to detect the
Because you task1 and task2 code will be executed
again in this task following the rules in section 4.1.
ning of Massive Datasets book for the algorithm
Spark RDD and standard Python or Scala libraries.
graphframes.
Caecueation (3 pts)
the betweenness of each edge in the originae
to save your result in a txt file. The format of
user_id1’, ‘user_id2’), betweenness vaeue
sorted by the betweenness values in the descending
tuple in eexicographicae order (the user_id is type
should also in eexicographicae order. You do not
Figure 2: betweenness output file format
(3 pts)
the graph into suitable communities, which reaches
formula of modularity is shown below:
Newman algorithm, after removing one edge, you should
the formula represents the edge number of the
adjacent matrix of the originae graph. (Hint:
be changed).
user node, we stiee regard it as a vaeid community.
in a txt file. The format is the same with the
the communities
separately, you
4.1. You can refer
details.
libraries. Remember to
originae graph you coneach line is
descending order and
type of string). The
not need to round
reaches the global
should re-compute
the originae graph.
In each remove
community.
output file from
Python:
spark-submit –packages graphframes:graphframes:0.6.0-spark2.3-s_2.11 task1.py <filter
threshold> <input_file_path> <community_output_file_path>
spark-submit task2.py <filter threshold> <input_file_path> <betweenness_output_file_path>
<community_output_file_path>
Scala:
spark-submit –packages graphframes:graphframes:0.6.0-spark2.3-s_2.11 –-class task1
hw4.jar <filter threshold> <input_file_path> <community_output_file_path>
spark-submit –-class task2 hw4.jar <filter threshold> <input_file_path>
<betweenness_output_file_path> <community_output_file_path>
Input parameters:
1. <filter threshold>: the filter threshold to generate edges between user nodes.
2. <input file path>: the path to the input file including path, file name and extension.
3. <betweenness output file path>: the path to the betweenness output file including path,
file name and extension.
4. <community output file path>: the path to the community output file including path, file
name and extension.
Execution time:
The overall runtime limit of your task1 (from reading the input file to finishing writing the
community output file) is 200 seconds.
The overall runtime limit of your task2 (from reading the input file to finishing writing the
community output file) is 250 seconds.
If your runtime exceeds the above limit, there will be no point for this task.
5. About Vocareum
a. You can use the provided datasets under the directory resource: /asnlib/publicdata/
b. You should upload the reSuired files under your workspace: work/
c. You must test your scripts on both the local machine and the Vocareum terminal before
submission.
d. During submission period, the Vocareum will automatically test task1 and task2.
e. During grading period, the Vocareum will use another dataset that has the same format
for testing.
f. We do not test the Scala implementation during the submission period.
g. Vocareum will automatically run both Python and Scala implementations during the grading period.
h. Please start your assignment early! You can resubmit any script on Vocareum. We will only
grade on your last submission.
6. Grading Criteria
(% penalty = % penalty of possible points you get)
a. You can use your free 8-day extension separately or together. You must submit a late-day
reSuest via https://forms.gle/worKTbCRBWKQ6jSu6. This form is recording the number of
late days you use for each assignment. By default, we will not count the late days if no
reSuest submitted.
b. There will be 10% bonus for each task if your Scala implementations are correct. Only
when your Python results are correct, the bonus of Scala will be calculated. There is no
partial point for Scala.
c. There will be no point if your submission cannot be executed on Vocareum.
d. There is no regrading. Once the grade is posted on the Blackboard, we will only regrade
your assignments if there is a grading error. No exceptions.
e. There will be 20% penalty for the late submission within one week and no point after that.

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