Foundations and Applications of Data Mining Assignment 4

DSCI-553 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 similar business tastes. 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 Requirements
a. For Task 1, you can use the Spark DataFrame and GraphFrames library. For task 2 you can ONLY use
Spark RDD and standard Python or Scala libraries. There will be a 10% bonus for each task if you also
submit a Scala implementation and both your Python and Scala implementations are correct.
2.2 Programming Environment
Python 3.6, JDK 1.8, Scala 2.12, and Spark 3.1.2
We will use these library versions to compile and test your code. There will be no point if we cannot run
your code on Vocareum.
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 required
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:
a. [REQUIRED] two Python scripts, named: task1.py, task2.py
b1. [OPTIONAL, REQUIRED FOR SCALA] two Scala scripts, named: task1.scala, task2.scala
b2. [OPTIONAL, 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 your code with our testing data (data will be in
the same format).
3. Datasets
We have generated a sub-dataset, ub_sample_data.csv, from the Yelp review dataset containing user_id
and business_id. You can find the data on Vocareum under resource/asnlib/publicdata/.
4. Tasks
4.1 Graph Construction
To construct the social network graph, assume that each node is uniquely labeled and that links are
undirected and unweighted.
Each node represents a user. There should be an edge between two nodes if the number of common
businesses reviewed by two users is greater than or equivalent to the filter threshold. For example,
suppose user1 reviewed set{business1, business2, business3} and user2 reviewed set{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 will not include that node in the graph.
The filter threshold will be given as an input parameter when running your code.
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.
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-guide-python.html
https://docs.databricks.com/spark/latest/graph-analysis/graphframes/user-guide-scala.html
4.2.1 Execution Detail
The version of the GraphFrames should be 0.6.0.
(For your convenience, graphframes0.6.0 is already installed for python on Vocareum. The corresponding
jar package can also be found under the $ASNLIB/public folder. )
For Python (in local machine):
● [Approach 1] Run “python3.6 -m pip install graphframes” in the terminal to install the package.
● [Approach 2] In PyCharm, you add the sentence below into your code to use the jar package
os.environ["PYSPARK_SUBMIT_ARGS"] = "--packages
graphframes:graphframes:0.8.2-spark3.1-s_2.12 pyspark-shell"
● In the terminal, you need to assign the parameter “packages” of the spark-submit:
--packages graphframes:graphframes:0.8.2-spark3.1-s_2.12
For Scala (in local machine):
● In Intellij IDEA, you need to add library dependencies to your project
“graphframes” % “graphframes” % “0.8.2-spark3.1-s_2.12”
“org.apache.spark” %% “spark-graphx” % sparkVersion
● In the terminal, you need to assign the parameter “packages” of the spark-submit:
--packages graphframes:graphframes:0.8.2-spark3.1-s_2.12
For the parameter “maxIter” of the LPA method, you should set it to 5.
4.2.2 Output Result
In this task, you need to save your result of communities in a txt file. Each line represents one
community and the format is:
‘user_id1’, ‘user_id2’, ‘user_id3’, ‘user_id4’, …
Your result should be firstly sorted by the size of communities in ascending order, and then the first
user_id in the community in lexicographical order (the user_id is of type string). The user_ids in each
community should also be in the lexicographical order.
If there is only one node in the community, we still regard it as a valid community.
Figure 1: community output file format
4.3 Task 2: Community Detection Based on Girvan-Newman algorithm (5 pts)
In task 2, you will implement your own Girvan-Newman algorithm to detect the communities in the
network graph. You can refer to Chapter 10 from the Mining of Massive Datasets book for the algorithm
details.
Because your task1 and task2 code will be executed separately, you need to construct the graph again in
this task following the rules in section 4.1.
For task 2, you can ONLY use Spark RDD and standard Python or Scala libraries. Remember to delete
your code that imports graphframes. Usage of Spark DataFrame is NOT allowed in this task.
4.3.1 Betweenness Calculation (2 pts)
In this part, you will calculate the betweenness of each edge in the original graph you constructed in 4.1.
Then you need to save your result in a txt file. The format of each line is
(‘user_id1’, ‘user_id2’), betweenness value
Your result should be firstly sorted by the betweenness values in descending order and then the first
user_id in the tuple in lexicographical order (the user_id is type of string). The two user_ids in each tuple
should also be in lexicographical order.
For output, you should use the python built-in round() function to round the betweenness value to five
digits after the decimal point. (Rounding is for output only, please do not use the rounded numbers for
further calculation)
IMPORTANT: Please strictly follow the output format since your code will be graded automatically. We
will not regrade because of formatting issues.
Figure 2: betweenness output file format
4.3.2 Community Detection (3 pts)
You are required to divide the graph into suitable communities, which reaches the global highest
modularity. The formula of modularity is shown below:
According to the Girvan-Newman algorithm, after removing one edge, you should re-compute the
betweenness. The “m” in the formula represents the edge number of the original graph. (Hint: In each
remove step, “m”, “k_i” and “k_j” should not be changed, while ‘A’ is calculated based on the updated
graph.). In the step of removing the edges with the highest betweenness, if two or more edges have the
same (highest) betweenness, you should remove all those edges.
If the community only has one user node, we still regard it as a valid community.
You need to save your result in a txt file. The format is the same as the output file from task 1.
(Hint: For the second part of task 2, you should take into account the precision. For eg: stop the
modularity calculation only if there is a significant reduction in the new modularity)
4.4 Execution Format
Execution example:
Python:
spark-submit --packages graphframes:graphframes:0.8.2-spark3.1-s_2.12 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:0.8.2-spark3.1-s_2.12 –-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 400 seconds.
The overall runtime limit of your task 2 (from reading the input file to finishing writing the community
output file) is 400 seconds.
If your runtime exceeds the above limit, there will be no point for this task.
5. About Vocareum
a. Dataset is under the directory $ASNLIB/publicdata/, jar package is under $ASNLIB/public/
b. You should upload the required files under your workspace: work/, and click submit
c. You should test your scripts on both the local machine and the Vocareum terminal before
submission.
d. During the submission period, the Vocareum will automatically test task1 and task2.
e. During the 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
5. Grading Criteria
(% penalty = % penalty of possible points you get)
1. You can use your free 5-day extension separately or together
a. https://forms.gle/edH8jw1mJjrLFRcm8
b. This form will record the number of late days you use for each assignment. We will not
count late days if no request is submitted. Remember to submit the request BEFORE
the deadline.
2. There will be a 10% bonus if you use both Scala and Python.
3. We 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.
4. All submissions will be graded on the Vocareum. Please strictly follow the format provided, otherwise
you can’t get the point even though the answer is correct.
5. If the outputs of your program are unsorted or partially sorted, there will be a 50% penalty.
6. We can regrade your assignments within seven days once the scores are released. No argument after
one week.
7. There will be a 20% penalty for late submission within a week and no point after a week.
8. Only when your results from Python are correct, the bonus of using Scala will be calculated. There is no
partial point for Scala.
7. Common problems causing fail submission on Vocareum/FAQ
(If your program runs seems successfully on your local machine but fail on Vocareum, please check
these)
1. Try your program on Vocareum terminal. Remember to set python version as python3.6,
Use the latest Spark
/opt/spark/spark-3.1.2-bin-hadoop3.2/bin/spark-submit
Select JDK 8 by running the command
"export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64"
2. Check the input command line formats.
3. Check the output formats, for example, the headers, tags, typos.
4. Check the requirements of sorting the results.
5. Your program scripts should be named as task1.py task2.py etc.
6. Check whether your local environment fits the assignment description, i.e. version, configuration.
7. If you implement the core part in python instead of spark, or implement it with a high time
complexity (e.g. search an element in a list instead of a set), your program may be killed on the
Vocareum because it runs too slow.
8. You are required to only use Spark RDD in order to understand Spark operations more deeply. You
will not get any points if you use Spark DataFrame or DataSet. Don’t import sparksql.
9. Do not use Vocareum for debugging purposes, please debug on your local machine. Vocareum can
be very slow if you use it for debugging.
10. Vocareum is reliable in helping you to check the input and output formats, but its function on
checking the code correctness is limited. It can not guarantee the correctness of the code even with
a full score in the submission report.
11. Some students encounter an error like: the output rate …. has exceeded the allowed
value ….bytes/s; attempting to kill the process.
To resolve this, please remove all print statements and set the Spark logging level such that it
limits the logs generated - that can be done using sc.setLogLevel . Preferably, set the log level to
either WARN or ERROR when submitting your code.