Description
2) Introduction
The students of North Shore High School are notoriously cliquey. Tina Fey and Tim Meadows try their best to
forge relationships between students, but students from one group rarely communicate with students from
another.
In this lab, your goal is to model the students of North Shore High School as a graph, and to explore different
ways to model the school’s social network based on queries to the graph.
3) Testing your lab
We’ve included a user interface to help visualize the problem. Run server.py and open your browser to
localhost:8000 (http://localhost:8000/) to see the UI.
As in the previous labs, we also provide you with a test.py script to help you verify the correctness of your
code.
4) Representation
You will need to devise an appropriate model of the high school’s social network. To get started, we will give
you a list of students, where each student is represented by a list [name, interest1, interest2, interest3,
…] .
4.1) Friendship
Two students are considered to be friends if they have at least one interest in common (however, students are
not considered to be friends with themselves). The weight of a friendship is how many interests that friendship
has in common.
For example, if Adam is interested in [‘fishing’, ‘video games’, ‘programming’, ‘fast food’,
‘football’, ‘music’] , and Glen Coco is interested in [‘programming’, ‘music’, ‘coffee’, ‘fishing’,
‘video games’] , then Adam and Glen Coco are friends, and the weight of their friendship is 4. That’s 4 for
you, Glen Coco. You go, Glen Coco.
In tiny.json , find and identify two people who are friends. Write a test case in test.py (in the TestTiny
class) to make sure those students are considered friends if they are both added to the school.
In tiny.json , what is the weight of the friendship between Cady and Gretchen? (After answering, add a
corresponding test case to test.py .)
We have provided two data sets for the school, found in the resources folder of the lab: tiny.json and
school.json . We recommend using the tiny.json data set to test your understanding of the concepts
presented here, and to write your own test cases for the lab.
5) Setting Up the School
We have provided a class called School in lab.py , to represent the school. Your job will be to complete the
methods therein according to the specification below.
5.1) Insertion
When a student enters North Shore, they are immediately categorized based on their hobbies and interests.
Based on these criteria, the student is then assigned to the appropriate cliques.
You may assume that student names are unique (i.e. no two students share a name). Your method of inserting
students should ensure that you find and create the appropriate friendships between students.
5.2) Deletion
When a student leaves the school, it is as though they never existed: they are wiped from the school database
and all cliques, and they leave no trace of any unique interests.
5.3) Updating
Students’ interests may change over time. We would like a means of representing these changes in our
school. When a student changes their interests, the weights of their friendships should change appropriately.
If Cady was updated so that her interests were [‘sports’, ‘math’] , who would her friends be? Enter your
answer as a Python list below (in any order). (Also, add a corresponding test case to test.py in TestTiny .)
6) Cliques
In high school, cliques are a hard problem to solve. In graph theory, they are, too (NP-hard
(https://en.wikipedia.org/wiki/NP-hardness), this is). In essence, the upshot is that there is no known way to
solve this problem efficiently.
A clique is defined as a subset of vertices in a graph such that every two vertices in the clique share an edge.
A maximal clique is a clique that cannot be extended any further by adding another vertex (i.e., a clique that is
not a subset of another clique).
It is your job to categorize students into cliques, where a school clique is a group of friends who are all friends
with each other. Here, we will only look for maximal cliques, as defined above. We will implement this
categorization through three methods:
get_cliques_for_student should return a list of the maximal cliques to which a given student belongs
get_cliques should find all the maximal cliques in the whole school
get_cliques_of_size_n should find all the maximal cliques in the school that have a given size
Importantly, you should make sure that the values returned by these methods account for students being
added and deleted from the school, or being updated.
Given the intractability of this problem, try to make your implementation of these methods as efficient as
possible (in particular, try to avoid recomputing the result to the same problem more than once).
Add at least one nontrivial test case for each of the methods above to the TestTiny class in test.py . Your
tests should test that the correct cliques are computed, even if students are added/removed/updated.
6.1) Independent Set
You were tasked with solving the clique problem faced by the North Shore High School. One idea you have is
to promote friendships between students in different cliques by introducing them to one another.
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\__=( o_O )=
(__________)
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of the GNU Affero General Public License, version 3 (https://6009.csail.mit.edu/cs_util/license).
(Download Source Code (https://6009.csail.mit.edu/cs_util/source.zip?course=fall17))
We hope that by targeting specific students and mixing them into new groups, we may be able to alleviate our
problem.
Luckily, graph theory will come to your aid once again! In graph theory, an independent set is the complement
of a clique: that is, it is a set of vertices in a graph, none of which are adjacent. A maximal independent set is
the complement of a maximal clique.
Implement find_independent_set , which should return a maximal independent set for a given student (i.e., a
set of students, none of whom are friends) that contains the given student. In particular, it should return the
largest possible independent set that contains that student.
As a note: once we had that independent set, we could, in principle, add a new shared interest between the
given student and the students in the independent set, creating a new maximal clique without expanding the
size of any existing clique.
Add at least one nontrivial test case for find_independent_set to the TestTiny class in test.py .
7) Code Submission
Select File No file selected
8) Checkoff
Once you are finished with the code, please come to a tutorial, lab session, or office hour and add yourself to
the queue asking for a checkoff. You must be ready to discuss your code and test cases in detail before
asking for a checkoff.
You should be prepared to demonstrate your code (which should be well-commented, should avoid repetition,
and should make good use of helper functions). In particular, be prepared to discuss:
the additional test cases you added to TestTiny
what values you stored in attribute variables of the School class. How did you represent students?
Friendships?
how the values you stored are updated when a student is added, deleted, or updated
Your implementation of get_cliques
Your implementation of get_cliques_for_student
Your implementation of get_cliques_of_size_n
Your implementation of find_independent_sets
8.1) Grade
You have not yet received this checkoff. When you have completed this checkoff, you will see a grade here.
