Assignment: PageRank Algorithm
Submission Deadline: Thursday, December 7, 2023, at noon
In this assignment it is not allowed to use the module Networkx.
This document is associated with the files pagerank.py and the data files:
web_stanford.txt, psh-uefa-2017-2018.csv, which can be obtained by
pulling your git repository. The file pagerank.py is the only one that
needs to be edited and submitted.
Task 1
Subtask 1.a
Write a class for representing directed graphs via their adjacency
matrices. (Note: the standard representation of an adjacency matrix for
directed graphs has “from” nodes on rows and “to” nodes on columns. However, in this
assignment as well as in the corresponding slides we use the convention
of having “from” nodes on columns and “to” nodes on rows.) The
constructor should accept an $n\times n$ adjacency matrix $A$ and a list
of node labels in string form (such as ['a', 'b', 'c', 'd'])
defaulting to None. Modify $A$ into $\widetilde{A}$ as shown in the
slides so that there are no sinks in the corresponding graph, then
calculate the $\widehat{A}$:
Save $\widehat{A}$ and the list of labels as attributes of the DiGraph
object. Use the integer numbers $[0,1,\ldots,n-1]$ as the labels only
if none are provided. Finally, raise a ValueError if the number of
labels is not equal to the number of nodes in the graph. (Hint: use
array broadcasting to compute $\widehat{A}$ efficiently. The Numpy
function where could help you.)
In the docstring example you find the same graph that was used in slides.
Subtask 1.b
Add the following methods to your class from the previous Subtask. Each should accept a damping factor $\epsilon$ (defaulting to 0.85), compute the PageRank vector $\mathbf{x}$, and return a dictionary mapping label $i$ to its PageRank value $x_i$.
1. linsolve(): solve for $\mathbf{x}$ in slide 27 after
substituting the fact that
$\mathbf{1}\mathbf{1}^{\mathsf{T}}\mathbf{x}=\mathbf{1}$ since
$\sum_i x_t(i)=1$:
2. eigensolve(): solve $\bar{A}\mathbf{x} = \mathbf{x}$ from
slide 28 for $\mathbf{x}$. Normalize the resulting eigenvector so that
its entries sum to $1$.
3. itersolve(): in addition to $\epsilon$, accept an
integer maxiter and a float tol. Iterate until
\(||\mathbf{x}_{t}- \mathbf{x}_{t-1}||_1 < \mathtt{tol} \; \text{or} \;
t > \mathtt{maxiter}\).
Use $\mathbf{x}_{0}=[\frac{1}{n},\frac{1}{n},\ldots,\frac{1}{n}]^{\mathsf{T}}$
as the initial vector (any positive vector that sums to $1$ will do,
but this assumes equal starting probabilities).
In the docstring example you can test that the three methods yield the same results. For the graph of the lecture with $\epsilon=0.85$, you should get the following dictionary mapping labels to PageRank values.
{'a': 0.095758635, 'b': 0.274158285, 'c': 0.355924792, 'd': 0.274158285}
Subtask 1.c
Write a function that accepts a dictionary mapping labels to PageRank
values, like the outputs in the previous Subtask, and returns a list of
labels sorted from highest to lowest value. (Hint: look for the
built-in function sorted().)
For the graph in the slides with $\epsilon=0.85$, the list is ['c',
'b', 'd', 'a'] (or ['c', 'd', 'b', 'a'], since b and d have the
same PageRank value).
Round the steady state probabilities to the 8th decimal and break ties
by lexicographic order (for example, the ranking for the graph in the
slides with $\epsilon=0.85$ must be ['c', 'b', 'd', 'a'] while
['c', 'd', 'b', 'a'] becomes an incorrect order).
Looking at the graph from the slides, it is easy to see why a has the
lowest PageRank value: the only other node that points to it is b. It
also makes sense that c has the highest ranking, since c and d
both have edges from the other three nodes pointing to them, but d
only has one edge (pointing to c), while c points to both b and
d. In other words, at each step d distributes all of its importance
to c, while c splits its importance between b and d.
Of course, constructing rankings is much more difficult to do by hand when there are more than just a few nodes in the graph.
Task 2
The file web_stanford.txt contains a subset of the information on
Stanford University
webpages and the
hyperlinks between them, gathered in 2002. Each line of the file is
formatted as a/b/c/d/e/f..., meaning the webpage with ID a has
hyperlinks to webpages with IDs b, c, d, and so on. In fact the
IDs are integer numbers but for the sake of this assignment, handle
them as strings.
Write a function that accepts a damping factor $\epsilon$ defaulting
to 0.85. Read the data and get a list of the $n$ unique page IDs in
the file (the labels). Construct the $n\times n$ adjacency matrix of
the graph where node $j$ points to node $i$ if webpage $j$ has a
hyperlink to webpage $i$. Use your class from Subtask 1.a and its
itersolve() method from Subtask 1.b to compute the PageRank
values of the webpages, then rank them with your function from Subtask
1.c. Return the ranked list of webpage IDs. (Hint: after constructing
the list of webpage IDs, make a dictionary that maps a webpage ID to its
index in the list. The values are the row/column indices in the
adjacency matrix for each label.)
With $\epsilon=0.85$, the top three ranked webpage IDs are '98595',
'32791', and '28392'. Note, break ties lexicographically, e.g., if
‘1123’ and '345' have the same score then rank ‘1123’ before
'345'.
Task 3
The file psh-uefa-2018-2019.csv contains data for men’s football
teams in Europe for the current season from the main leagues in Italy,
England, France, Scotland, Spain, Germany, Greece, Belgium, Holland,
Portugal, Turkey, together with Champions League and Europa
League. Each line represents a different game, formatted
home_team,away_team,home_goals,away_goals. Write a function that
accepts a filename and a damping factor $\epsilon$ defaulting to
0.85. Read the specified file and get a list of the $n$ unique teams
in the file. Construct the $n\times n$ adjacency matrix of the graph
where node $j$ points to node $i$ with weight $w$ if team $j$ was
defeated by team $i$ in $w$ games. That is, edges point from losers
to winners. Ignore draw games. Use your class from Subtask 1.a and
its itersolve() method from Subtask 1.b to compute the PageRank
values of the teams, then rank them with your function from Subtask
1.c. Return the ranked list of team names.
Using psh-uefa-2018-2019.csv with $\epsilon=0.85$,
the top three ranked teams in the current season should be
Liverpool, Athletic Madrid, Paris SG.
The damping factor $\epsilon$ acts as an ``upset’’ factor: a larger $\epsilon$ puts more emphasis on win history; a smaller $\epsilon$ allows more randomness in the system, giving underdog teams a higher probability of defeating a team with a better record.
Note that we did not take into account weights derived from home-away situations, goal difference, importance of the match and aggregate results from Europa and Champions league.
It is also worth noting that the sink-fixing procedure is still
reasonable for this model because it gives every other team
equal likelihood of beating an undefeated team. That is, the
additional arcs don’t provide an extra advantage to any one team.
If you are interested in comparing this way of ranking against the FIFA international ranking for national teams, you can run your procedures on the data set avaialble from kaggle, which contains the results of international football matches of national teams from 1872 to 2021.