COMP2521

  • May 15, 2020

  2019/8/5 COMP2521 19T2 – Assignment 2
www.cse.unsw.edu.au/~cs2521/19T2/assigns/ass2/Ass2.html#changelog 1/6
COMP2521: Assignment 2
Simple Search Engine(s)
[The specification may change, Please check the change log on this page.]
Change log:
[07:00pm 01/Aug]: For Part-1, tolerance of 10e-4 is fine (read the post on the forum for clarifications).
[07:00pm 01/Aug]: For Part-2, you can assume that max line length of file “invertedIndex.txt” is 1,000 chars.
You can assume that you will have no duplicates for search terms, so “mars mars” is not possible.
You should read the file “invertedIndex.txt ” line by line, can assume max line length of 1,000 chars. And later tokenise
words (hint: use the function ” strtok ” from the sting.h lib) for more info see sample example-1 or sample example-2.
[05:15pm 25/Jul]: Added: Hints on Wout
: How to calculate?
[05:15pm 25/Jul]: Marks for each part is now out of 20. There will be 4 marks for “Style and Complexity”. So the total
assignment marks are 24 (mapped to 14 marks towards total course mark for ass2).
[08:00am 24/Jul]: “invertedIndex.txt” file is updated in ex1.zip, ex2.zip and ex2.zip files. you do not need to print
extension “.txt”.
Objectives
to implement simple search engines using well known algorithms like (Weighted) PageRank and rank aggregation, simplified
versions for this assignment!
to give you further practice with C and data structures (Graph ADT)
Admin
Marks 24 marks
(part-1: 11 marks, part-2: 4 marks, part-3: 5 marks and 4 marks for “Style and Complexity”)
(14 marks towards total course mark).
Individual Assignment This assignment is an individual assignment.
Due 05pm Thursday Week-10
Late
Penalty
2 marks per day off the ceiling.
Last day to submit this assignment is 05pm Monday Week-11, of course with late penalty.
Submit Read instructions in the “Submission” section below.
Aim
In this assignment, your task is to implement simple search engines using the well known algorithm (Weighted) PageRank, simplified for
this assignment, of course! You should start by reading the wikipedia entries on these topics. Later I will also discuss these topics in the
lecture.
PageRank (read up to the section “Damping factor”)
The main focus of this assignment is to build a graph structure and calculate Weighted PageRanks, and rank pages based on these
values. You don’t need to spend time crawling, collecting and parsing weblinks for this assignment. You will be provided with a collection
of “web pages” with the required information for this assignment in a easy to use format. For example, each page has two sections,
Section-1 contains urls representing outgoing links. Urls are separated by one or more blanks, across multiple lines.
Section-2 contains selected words extracted from the url. Words are separated by one or more spaces, spread across multiple
lines.
Hint: If you use fscanf to read the body of a section above, you do not need to impose any restriction on line length. I suggest you should
try to use this approach – use fscanf! However, if you want to read line by line using say fgets, you can assume that maximum length of
a line would be 1000 characters.
Example file url31.txt
#start Section-1
url2 url34 url1 url26
url52 url21
url74 url6 url82
#end Section-1
#start Section-2
Mars has long been the subject of human interest. Early telescopic observations
revealed color changes on the surface that were attributed to seasonal vegetation
and apparent linear features were ascribed to intelligent design.
2019/8/5 COMP2521 19T2 – Assignment 2
www.cse.unsw.edu.au/~cs2521/19T2/assigns/ass2/Ass2.html#changelog 2/6
#end Section-2
Summary
In Part-1: You need to create a graph structure that represents a hyperlink structure of given collection of “web pages” and
for each page (node in your graph) calculate Weighted PageRank and other graph properties.
In Part-2: Search Engine : Graph structure-based search engine.
In Part-3: Rank Aggregation (Hybrid search engine), you need to combine multiple ranks (for example, PageRank and tf-idf
values) in order to rank pages.
Additional files: You can submit additional supporting files, *.cand *.h, for this assignment. For example, you may
implement your graph adt in files graph.c and graph.h and submit these two files along with other required files as mentioned below.
Sample files
Sample1.zip
Part-1: Graph structure-based Search Engine (11 marks)
Read the following for this part:
Hints on “How to Implement Ass2 (Part-1)” , to be discussed in the lecture.
Weighted PageRank Algorithm (paper)
Hints on Wout
: How to calculate?
Calculate Weighted PageRanks
You need to write a program in the file pagerank.c that reads data from a given collection of pages in the file collection.txt and builds a
graph structure using Adjacency Matrix or List Representation. Using the algorithm described below, calculate Weighted PageRank for
every url in the file collection.txt. In this file, urls are separated by one or more spaces or/and new line character. Add suffix .txt to a
url to obtain file name of the corresponding “web page”. For example, file url24.txt contains the required information for url24.
Example file collection.txt
url25 url31 url2
url102 url78
url32 url98 url33
Simplified Weighted PageRank Algorithm you need to implement (for this assignment) is shown below. Please note that the formula to
calculate PR values is slightly different to the one provided in the corresponding paper (for explanation, read Damping factor).
PageRankW(d, diffPR, maxIterations)
Read “web pages” from the collection in file “collection.txt”
and build a graph structure using Adjacency List Representation
N = number of urls in the collection
For each url pi in the collection
End For
iteration = 0;
diff = diffPR; // to enter the following loop
While (iteration < maxIteration AND diff >= diffPR)
2019/8/5 COMP2521 19T2 – Assignment 2
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iteration++;
End While
Your program in pagerank.c will take three arguments (d – damping factor, diffPR – difference in PageRank sum, maxIterations – maximum iterations) and using the algorithm described in this section, calculate Weighted PageRank for every url.
For example,
% pagerank 0.85 0.00001 1000
Your program should output a list of urls in descending order of Weighted PageRank values (use format string “%.7f” to 8 significant
digits) to a file named pagerankList.txt. The list should also include out degrees (number of out going links) for each url, along with its Weighted PageRank value. The values in the list should be comma separated. For example, pagerankList.txt may contain the
following:
Example file pagerankList.txt
url31, 3, 0.2623546
url21, 1, 0.1843112
url34, 6, 0.1576851
url22, 4, 0.1520093
url32, 6, 0.0925755
url23, 4, 0.0776758
url11, 3, 0.0733884
Sample Files for 1A
You can download the following three sample files with expected pagerankList.txt files. For your reference, I have also
included the file “log.txt” which includes values of Win, Wout, etc. Please note that you do NOT need to generate such a
log file.
Use format string “%.7f” to output pagerank values. Please note that your pagerank values might be slightly different to that
provided in these samples. This might be due to the way you carry out calculations. However, make sure that your pagerank
values match to say first 6 decimal points to the expected values. For example, say an expected value is 0.1843112, your
value could be 0.184311x where x could be any digit.
All the sample files were generated using the following command:
% pagerank 0.85 0.00001 1000
ex1
ex2
ex3
Part-2: Search Engine (4 marks)
Your program must use data available in two files invertedIndex.txt and pagerankList.txt, and must derive result from them for this
part. We will test this program independently to your solutions for other sections.
The file named invertedIndex.txt contains one line per word, words should be alphabetically ordered, using ascending order. Each list
of urls (for a single word) are alphabetically ordered, using ascending order. Example file invertedIndex.txt
2019/8/5 COMP2521 19T2 – Assignment 2
www.cse.unsw.edu.au/~cs2521/19T2/assigns/ass2/Ass2.html#changelog 4/6
design url2 url25 url31
mars url101 url25 url31
vegetation url31 url61
Write a simple search engine in file searchPagerank.c that given search terms (words) as commandline arguments, finds pages with one
or more search terms and outputs (to stdout) top 30 pages in descending order of number of search terms found and then within each
group, descending order of Weighted PageRank. If number of matches are less than 30, output all of them.
Example:
% searchPagerank mars design
url31
url25
url2
url101
Part-3: Hybrid/Meta Search Engine using Rank Aggregation (5 marks)
In this part, you need to combine search results (ranks) from multiple sources (say from Part-1 and assignment-1) using “Scaled
Footrule Rank Aggregation” method, described below. All the required information for this method are provided below.
Let T1 and T2 are search results (ranks) obtained using two different criteria (say Part-1 and Part-2). Please note that we could use any
suitable criteria, including manually generated rank lists.
A weighted bipartite graph for scaled footrule optimization (C,P,W) is defined as,
C = set of nodes to be ranked (say a union of T1 and T2)
P = set of positions available (say {1, 2, 3, 4, 5})
W(c,p) is the scaled-footrule distance (from T1 and T2
) of a ranking that places element ‘c’ at position ‘p’, given by
where
n is the cardinality (size) of C,
|T1
| is the cardinality (size) of T1
,
|T2
| is the cardinality (size) of T2
,
T1
(x3
) is the position of x3
in T1
,
k is number of rank lists.
For example,
The final ranking is derived by finding possible values of position ‘P’ such that the scaled-footrule distance is minimum. There are many different ways to assign possible values for ‘P’. In the above example P = [1, 3, 2, 5, 4]. Some other possible values are, P = [1, 2,
4, 3, 5], P = [5, 2, 1, 4, 3], P = [1, 2, 3, 4, 5], etc. For n = 5, possible alternatives are 5! For n = 10, possible alternatives would be 10! that
is 3,628,800 alternatives. A very simple and obviously inefficient approach could use brute-force search and generate all possible
alternatives, calculate scaled-footrule distance for each alternative, and find the alternative with minimum scaled-footrule distance.
If you use such a brute-force search, you will receive maximum of 65% for Part-3. However, you will be rewarded 100% for Part-3 if you
implement a “smart” algorithm that avoids generating unnecessary alternatives, in the process of finding the minimum scaled-footrule
distance. Please document your algorithm such that your tutor can easily understand your logic, and clearly outline how you plan to
reduce search space, otherwise you will not be awarded mark for your “smart” algorithm! Yes, it’s only 35% of part-3 marks, but if you try
it, you will find it very challenging and rewarding.
Write a program scaledFootrule.c that aggregates ranks from files given as commandline arguments, and output aggregated rank list
with minimum scaled footrule distance.
How to Get Started, Part-3
Example, file rankA.txt
2019/8/5 COMP2521 19T2 – Assignment 2
www.cse.unsw.edu.au/~cs2521/19T2/assigns/ass2/Ass2.html#changelog 5/6
url1
url3
url5
url4
url2
Example, file rankD.txt
url3
url2
url1
url4
The following command will read ranks from files “rankA.txt” and “rankD.txt” and outputs minimum scaled footrule distance (using format
%.6f) on the first line, followed by the corresponding aggregated rank list.
% scaledFootrule rankA.txt rankD.txt
For the above example, there are two possible answers, with minimum distance of 1.400000.
Two possible values of P with minnimum distance are:
C = [url1, url2, url3, url4, url5]
P = [1, 4, 2, 5, 3] and
P = [1, 5, 2, 4, 3]
By the way, you need to select any one of the possible values of P that has minium distance, so there could be multiple possible
answers. Note that you need to output only one such list.
One possible answer for the above example, for P = [1, 4, 2, 5, 3] :
1.400000
url1
url3
url5
url2
url4
Another possible answer for the above example, P = [1, 5, 2, 4, 3] :
1.400000
url1
url3
url5
url4
url2
Please note that your program should also be able to handle multiple rank files, for example:
% scaledFootrule rankA.txt rankD.txt newSearchRank.txt myRank.txt
Submission
Additional files: You can submit additional supporting files, *.c and *.h, for this assignment.
IMPORTANT: Make sure that your additional files (*.c) DO NOT have “main” function.
For example, you may implement your graph adt in files graph.c and graph.h and submit these two files along with other required files as mentioned below. However, make sure that these files do not have “main” function.
I explain below how we will test your submission, hopefully this will answer all of your questions.
You need to submit the following files, along with your supporting files (*.c and *.h):
pagerank.c
searchPagerank.c
scaledFootrule.c
Now say we want to mark your pagerank.c program. The auto marking program will take all your supporting files (other *.h and *.c) files,
along with pagerank.c and execute the following command to generate executable file say called pagerank. Note that the other four files
from the above list (searchPagerank.c and scaledFootrule.c) will be removed from the dir:
% gcc -Wall -lm -std=c11 *.c -o pagerank
2019/8/5 COMP2521 19T2 – Assignment 2
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So we will not use your Makefile (if any). The above command will generate object files from your supporting files and the file to be
tested (say pagerank.c), links these object files and generates executable file, say pagerank in the above example. Again, please make
sure that you DO NOT have main function in your supporting files (other *.c files you submit).
We will use similar approach to generate other executables (from searchPagerank.c and scaledFootrule.c).
How to Submit
Later, instructions on how to submit will be posted here.
Plagiarism
This is an individual assignment. You are not allowed to use code developed by persons other than you. In particular, it is not permitted to
exchange code or pseudocode between students. You are allowed to use code from the course material (for example, available as part
of the labs, lectures and tutorials). If you use code from the course material, please clearly acknowledge it by including a comment(s) in
your file. If you have questions about the assignment, ask your tutor.
Before submitting any work you should read and understand the sub section named Plagiarism in the section “Student Conduct” in the
course outline. We regard unacknowledged copying of material, in whole or part, as an extremely serious offence. For further information,
see the course outline.
— end —  

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