Foundations and Applications of Data Mining Assignment 4

INF553 Foundations and Applications of Data Mining

Assignment 4

NO LATE SUBMISSIONS
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 Requirements
a. You must use Python and Spark to implement all 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 library for task1, but for task2 you
can ONLY use Spark RDD and standard Python or Scala libraries. (ps. For Scala, you can try
GraphX, but for the assignment, you need to use GraphFrames.)
2.2 Programming Environment
Python 3.6, Scala 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 local machine and the Vocareum terminal 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
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: (all lowercase)
a. [REQUIRED] two Python scripts, named: task1.py, task2.py
b1. [REQUIRED FOR SCALA] two Scala scripts, named: task1.scala, task2.scala
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 equivalent 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 will not include that node in the graph.
In this assignment, we use filter threshold 7.
Use user_id directly as strings when constructing the graph, don’t hash them to integers.
4.2 Task1: Community Detection Based on GraphFrames (4 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-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 Python:
• In PyCharm, you need to add the sentence below into your code
pip install graphframes
os.environ["PYSPARK_SUBMIT_ARGS"] = (
"--packages graphframes:graphframes:0.6.0-spark2.3-s_2.11")
• In the terminal, you need to assign the parameter “packages” of the spark-submit:
--packages graphframes:graphframes:0.6.0-spark2.3-s_2.11
For Scala:
• In Intellij IDEA, you need to add library dependencies to your project
“graphframes” % “graphframes” % “0.6.0-spark2.3-s_2.11”
“org.apache.spark” %% “spark-graphx” % sparkVersion
• In the terminal, you need to assign the parameter “packages” of the spark-submit:
--packages graphframes:graphframes:0.6.0-spark2.3-s_2.11
For the parameter “maxIter” of 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 the ascending order and then
the first user_id in the community in lexicographical order (the user_id is type of 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 Task2: Community Detection Based on Girvan-Newman algorithm (8.5 pts)
In task2, you will implement your own Girvan-Newman algorithm to detect the communities in
the network graph. Because you 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. You can refer to the
Chapter 10 from the Mining of Massive Datasets book for the algorithm details.
For task2, you can ONLY use Spark RDD and standard Python or Scala libraries. Remember to
delete your code that imports graphframes.
4.3.1 Betweenness Calculation (4 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 the 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 in lexicographical order. You do not need to round your
result.
Figure 2: betweenness output file format
4.3.2 Community Detection (4.5 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.
The “A” in the formula is the adjacent matrix of the original graph. (Hint: In each remove step,
“m” and “A” should not be changed).
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 with the output file from
task1.
4.4 Execution Format
Execution example:
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 required 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. 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.
b. There will be no point if your submission cannot be executed on Vocareum.
c. 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.
d. No late submissions allowed.