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DSCI-553 Foundations and Applications of Data Mining

创建日期：2024-08-06 17:29:41

所属分类：计算机computer science

标签： DSCI-553

Foundations and Applications of Data Mining

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DSCI-553 Foundations and Applications of Data Mining

Assignment

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.

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