In this project we will explore graph theory theorems and algorithms,
by applying them on real data. In the first part of the project, we consider a particular graph which models correlations between stock price
time series. In the second part, we analyse traffic data on a dataset
provided by Uber.
1 Stock Market
In this part of the project, we study data from stock market. The data
is available on this Dropbox Link. The goal of this part is to study
correlation structures among fluctuation patterns of stock prices using
tools from graph theory. The intuition is that investors will have similar strategies of investment for stocks that are effected by the same
economic factors. For example, the stocks belonging the transportation sector may have different absolute prices, but if for example fuel
prices change or are expected to change significantly in the near future,
then you would expect the investors to buy or sell all stocks similarly
and maximize their returns. Towards that goal, we construct different
graphs based on similarities among the time series of returns on different stocks at different time scales (day vs a week). Then, we study properties of such graphs. The data is obtained from Yahoo Finance website
for 3 years. You’re provided with a number of csv tables, each containing several fields: Date, Open, High, Low, Close, Volume, and Adj Close
price. The files are named according to Ticker Symbol of each stock. You
may find the market sector for each company in Name_sector.csv.
1.1 Return correlation
In this part of the project, we will compute the correlation among lognormalized stock-return time series data. Before giving the expression
for correlation, we introduce the following notation:
• pi(t) is the closing price of stock i at the t
• qi(t) is the return of stock i over a period of [t − 1, t]
qi(t) = pi(t) − pi(t − 1)
pi(t − 1)
• ri(t) is the log-normalized return stock i over a period of [t − 1, t]
ri(t) = log(1 + qi(t))
Then with the above notation, we define the correlation between the
log-normalized stock-return time series data of stocks i and j as
hri(t)rj (t)i − hri(t)ihrj (t)i
2i − hri(t)i
2i − hrj (t)i
where h·i is a temporal average on the investigated time regime (for our
data set it is over 3 years).
Question 1: Provide an upper and lower bound on ρij . Also, provide
a justification for using log-normalized return (ri(t)) instead of regular
1.2 Constructing correlation graphs
In this part,we construct a correlation graph using the correlation coefficient computed in the previous section. The correlation graph has
the stocks as the nodes and the edge weights are given by the following
2(1 − ρij )
Compute the edge weights using the above expression and construct
the correlation graph.
Question 2: Plot the degree distribution of the correlation graph and
a histogram showing the un-normalized distribution of edge weights.
1.3 Minimum spanning tree (MST)
In this part of the project, we will extract the MST of the correlation
graph and interpret it.
Question 3: Extract the MST of the correlation graph. Each stock can
be categorized into a sector, which can be found in Name_sector.csv
file. Plot the MST and color-code the nodes based on sectors. Do you
see any pattern in the MST? The structures that you find in MST are
called Vine clusters. Provide a detailed explanation about the pattern
1.4 Sector clustering in MST’s
In this part, we want to predict the market sector of an unknown stock.
We will explore two methods for performing the task. In order to eval3
uate the performance of the methods we define the following metric
P(vi ∈ Si)
where Si is the sector of node i. Define
P(vi ∈ Si) = |Qi
where Qi is the set of neighbors of node i that belong to the same sector
as node i and Ni is the set of neighbors of node i. Compare α with the
P(vi ∈ Si) = |Si
Question 4: Report the value of α for the above two cases and provide
an interpretation for the difference.
1.5 Correlation graphs for weekly data
In the previous parts, we constructed the correlation graph based on
daily data. In this part of the project, we will construct a correlation
graph based on weekly data. To create the graph, sample the stock data
weekly on Mondays and then calculate ρij using the sampled data. If
there is a holiday on a Monday, we ignore that week. Create the correlation graph based on weekly data.
Question 5: Extract the MST from the correlation graph based on weekly
data. Compare the pattern of this MST with the pattern of the MST
found in question 3.
2 Let’s Help Santa!
Companies like Google and Uber have a vast amount of statistics about
transportation dynamics. Santa has decided to use network theory to
facilitate his gift delivery for the next christmas. When we learned
about his decision, we designed this part of the project to help him. We
will send him your results for this part!
2.1 Download the Data
Go to “Uber Movement” website and download data of Monthly Aggregate (all days), 2017 Quarter 4, for San Francisco area 1
dataset contains pairwise traveling time statistics between most pairs
of points in San Francisco area. Points on the map are represented by
unique IDs. To understand the correspondence between map IDs and
areas, download Geo Boundaries file from the same website 2
file contains latitudes and longitudes of the corners of the polygons circumscribing each area. In addition, it contains one street address inside each area, referred to as DISPLAY_NAME. To be specific, if an area
is represented by a polygon with 5 corners, then you have a 5×2 matrix
of the latitudes and longitudes, each row of which represents latitude
and longitude of one corner.
2.2 Build Your Graph
Read the dataset at hand, and build a graph in which nodes correspond
to locations, and undirected weighted edges correspond to the mean
traveling times between each pair of locations (only December). Add
the following attributes to the vertices:
1. Display name: the street address
2. Location: mean of the coordinates of the polygon’s corners (a 2-D
If you download the dataset correctly, it should be named as
2The file should be named SAN_FRANCISCO_CENSUSTRACTS.JSON
The graph will contain some isolated nodes (extra nodes existing in the
Geo Boundaries JSON file) and a few small connected components. Remove such nodes and just keep the giant connected component of the
graph. In addition, merge duplicate edges by averaging their weights
. We will refer to this cleaned graph as G afterwards.
Question 6: Report the number of nodes and edges in G.
2.3 Traveling Salesman Problem
Question 7: Build a minimum spanning tree (MST) of graph G. Report
the street addresses of the two endpoints of a few edges. Are the results
Question 8: Determine what percentage of triangles in the graph (sets
of 3 points on the map) satisfy the triangle inequality. You do not need
to inspect all triangles, you can just estimate by random sampling of
Now, we want to find an approximation solution for the traveling salesman problem (TSP) on G. Apply the 2-approximate algorithm described
in the class4
. Inspect the sequence of street addresses visited on the
map and see if the results are intuitive.
Question 9: Find the empirical performance of the approximate algorithm:
Approximate TSP Cost
Optimal TSP Cost
Question 10: Plot the trajectory that Santa has to travel!
3Duplicate edges may exist when the dataset provides you with the statistic of a road in both directions. We remove duplicate edges for the sake of simplicity.
4You can find the algorithm in: Papadimitriou and Steiglitz, “Combinatorial optimization: algorithms and complexity”, Chapter 17, page 414
3 Analysing the Traffic Flow
Next December, there is going to be a sport event between Stanford
University and University of California, Santa Cruz (UCSC). A large
number of students are enthusiastic about the event, which is going
to be held in UCSC. Stanford fans want to drive from their campus to
the rival’s. We would like to analyse the maximum traffic that can flow
from Stanford to UCSC.
3.1 Estimate the Roads
We want to estimate the map of roads without using actual road datasets.
Educate yourself about Delaunay triangulation algorithm and then apply it to the nodes coordinates5
Question 11: Plot the road mesh that you obtain and explain the result.
Create a subgraph G∆ induced by the edges produced by triangulation.
3.2 Calculate Road Traffic Flows
Question 12: Using simple math, calculate the traffic flow for each road
in terms of cars/hour.
Hint: Consider the following assumptions:
• Each degree of latitude and longitude ≈ 69 miles
• Car length ≈ 5 m = 0.003 mile
• Cars maintain a safety distance of 2 seconds to the next car
• Each road has 2 lanes in each direction
5You can use scipy.spatial.Delaunay in python
Assuming no traffic jam, consider the calculated traffic flow as the max
capacity of each road.
3.3 Calculate the Max Flow
Consider the following addresses:
• Source address: 100 Campus Drive, Stanford
• Destination address: 700 Meder Street, Santa Cruz
Question 13: Calculate the maximum number of cars that can commute per hour from Stanford to UCSC. Also calculate the number of
edge-disjoint paths between the two spots. Does the number of edgedisjoint paths match what you see on your road map?
3.4 Defoliate Your Graph
In G∆, there are a number of unreal roads that could be removed. For
instance, there are many fake bridges crossing the bay. Apply a threshold on the travel time of the roads in G∆ to remove the fake edges. Trim
the fake edges and call the resulting graph G˜∆.
Question 14: Plot G˜∆ on real map coordinates. Are real bridges preserved?
Hint: You can consider the following coordinates:
• Golden Gate Bridge: [[-122.475, 37.806], [-122.479, 37.83]]
• Richmond, San Rafael Bridge: [[-122.501, 37.956], [-122.387, 37.93]]
• San Mateo Bridge: [[-122.273, 37.563], [-122.122, 37.627]]
• Dambarton Bridge: [[-122.142, 37.486], [-122.067, 37.54]]
• San Francisco – Oakland Bay Bridge: [[-122.388, 37.788], [-122.302,
Question 15: Now, repeat question 8 for G˜∆ and report the results. Do
you see any significant changes?