Link Analysis

History

Web page organization in the past

Web pages were manually curated and organized.
Does not scale.

Next, web search engines were developed: information retrieval
Information retrieval focuses on finding documents from a trusted set.

Challenges for web search

Who to trust given the massive variety of information sources?
- Trustworthy pages may point to each other.
What is the best answer to a keyword search (given a context)?
- Pages using the keyword in the same contexts tend to point to each other.
All pages are not equally important.
- Web pages/sites on the Internet can be represented as a graph:
Web pages/sites are nodes on the graph.
Links from one page to another represents the incoming/outgoing connections between nodes.
We can compute the importance of nodes in this graph based on the distribution/intensity of these links.

PageRank

Initial formulation

Link as votes:
- A page (node) is more important if it has more links.
  - Incoming or outgoing?
Think of incoming links as votes.
- Are all incoming links equals?
  - Links from important pages count more.

graph TD
    B(("B(38.4)")):::bWide --> C(("C(34.3)")):::cWide
    C --> B
    D(("D(3.9)")):::dWide --> A(("A(3.3)")):::aWide
    D --> B
    E(("E(8.1)")):::eWide --> D
    E --> B
    E --> F(("F(3.9)")):::dWide
    F --> B
    F --> E
    G(("1.6")):::wide5 --> B
    H(("1.6")):::wide5 --> C
    I(("1.6")):::wide5 --> B
    I --> E
    K(("1.6")):::wide5 --> B
        
    classDef aWide padding:10px
    classDef bWide padding:38px
    classDef cWide padding:34px
    classDef aWide padding:10px
    classDef eWide padding:15px
    classDef wide5 padding: 5px

    style A fill:#FFA500
    style B fill:#00FFFF
    style C fill:#8B8000
    style D fill:#f198b3
    style E fill:#ADEBB3
    style F fill:#f198b3

Each link’s vote is proportional to the importance of its source page.
If page j with importance $r_j$ has n outgoing links, then each outgoing link has $r_j$ votes.
The importance of page j is the sum of the votes on its incoming links.

graph TD
    k((k)) -->|$$r_k/4$$| j((j))
    i((i)) -->|$$r_i/3$$| j((j))
    s1((" ")) --> i
    i --> s2((" "))
    i --> s3((" "))
    s4((" ")) --> k
    k --> s5((" "))
    k --> s6((" "))
    k --> s7((" "))
    j -->|$$r_j/3$$| s8((" "))
    j -->|$$r_j/3$$| s9((" "))
    j -->|$$r_j/3$$| s10((" "))
    s8 --> s81((" "))
    s8 --> s82((" "))
    s8 --> s83((" "))
    s9 --> s91((" "))
    s9 --> s92((" "))
    s9 --> s93((" "))
    s10 --> s101((" "))
    s10 --> s102((" "))
    s10 --> s103((" "))
    style j fill:#FF0000
    style i fill:#ADEBB3
    style k fill:#ADEBB3
    style s1 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s2 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s3 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s4 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s5 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s6 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s7 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s81 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s82 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s83 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s91 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s92 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s93 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s101 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s102 fill:#FFFFFF00, stroke:#FFFFFF00;
    style s103 fill:#FFFFFF00, stroke:#FFFFFF00;

\[r_j = \frac{r_i}{3} + \frac{r_k}{4}\]

The flow model

Summary:
- A vote from an important page is worth more.
- A page is important if it is linked to by other important pages.
Imagine the web in 1839!
- Three sites: y, m, a
- y links to a and to itself.
- a links to y and m.
- m links to a.

flowchart TD
  Y((y)) -->|y/2| Y((y));
  Y -->|y/2| A((a));
  A -->|a/2| Y;
  A -->|a/2| M((m));
  M -->|m| A;

$r_{y}$, $r_{a}$, and $r_{m}$ are the rank of nodes $y$, $a$, and $m$ respectively.
- Node $y$ has two outgoing links, so the weight of each link is going to be $r_{y}/2$.
- Node $a$ has two outgoing links, so the weight of each link is going to be $r_{a}/2$.
- Node $m$ has one outgoing link and the weight of this link is $r_{m}$.
From the above link graph, we can form the following flow equations:
- Node $y$ receives two incoming links, one from itself and one from node $a$:
  - $r_{y} = r_{y}/2 + r_{a}/2$
- Node $a$ receives two incoming links, one from node $y$ and one from node $m$:
  - $r_{a} = r_{y}/2 + r_{m}$
- Node $m$ receives one incoming link from node $a$:
  - $r_{m} = r_{a}/2$

General equation for rank calculation

The above example demonstrates the idea of a general equation for calculating rank $r_{j}$ for page j:

\[r_j = \sum\limits_{i\rightarrow j}^{n} \frac{r_i}{d_i}\]

$d_i$ is the number of out-degree (outgoing links) of node $i$

From the above link graph, we seem to have three equations and three unknown.
- In reality, after some algebraic simplication, we actually onle have two equations.
- Therefore, there is no unique solution at this point.
We need to impose one additional constraint to force uniqueness:
- $r_{y} + r_{a} + r_{m} = 1$
Solving this system of equations using Gaussian elimination, we have:
- $r_{y} = 2/5$
- $r_{a} = 2/5$
- $r_{m} = 1/5$
Does not scale to Internet-size!

Matrix formulation

Using the coefficients from the above flow equations, we can set up a stochastic adjacency matrix M
Matrix M of size N: N is the number of nodes in the graph (think number of web pages on the Internet).
Let page i has $d_{i}$ outgoing links.
- If there is an outgoing link from page i to another page j,
  - then $M_{ij} = 1/d_{i}$
  - else $M_{ij} = 0$
- Stochastic: sum of all values in a column of M is equal to 1.
- Adjacency: non-zero value indicates the availability of a link.
Rank vector $r$: Each element in $r$ represents the ranking value of one page.
- The order of pages in the rank vector should be the same as the order of pages in rows and columns of M.
Combining the stochastic adjacency matrix M (coefficient matrix) and the rank vector $r$, we can formulate a general equation for calculating rank r_j for page $j$ with regard to all pages:

\[r_j = \sum\limits_{i=0}^{N-1} M_{ij}r_{j}\]

This equation can be rewritten using the matrix form:

\[r = M \cdot r\]

Visualization

Suppose page i has importance $r_{i}$ and has outgoing links to three other pages, including page j.

Example of matrix formulation

Revisiting our flow equations from Section 2.2:

\[\begin{align} \left \{ \begin{aligned} r_{y} &= r_{y}/2 + r_{a}/2\\ r_{a} &= r_{y}/2 + r_{m}\\ r_{m} &= r_{a}/2 \end{aligned} \right. \end{align}\]

We will rewrite the above equations to highlight all coefficients.

\[\begin{align} \left \{ \begin{aligned} r_{y} &= r_{y}\times 1/2 + r_{a}\times 1/2 + r_{m}\times 0\\ r_{a} &= r_{y}\times 1/2 + r_{a}\times 0 \ \ \ \ + r_{m}\\ r_{m} &= r_{y}\times 0 \ \ \ \ + r_{a}\times 1/2 + r_{m}\times 0 \end{aligned} \right. \end{align}\]

Stochastic adjacency matrix M:

\[\left[ \begin{array}{ccc} 1/2 & 1/2 & 0 \\ 1/2 & 0 & 1 \\ 0 & 1/2 & 0 \end{array} \right]\]

Rank vector r:

\[\left[ \begin{array}{c} y \\ a \\ m \end{array} \right]\]

Dot product of rank vector and coefficient matrix:

\[\left[ \begin{array}{c} y \\ a \\ m \end{array} \right] = \left[ \begin{array}{ccc} 1/2 & 1/2 & 0 \\ 1/2 & 0 & 1 \\ 0 & 1/2 & 0 \end{array} \right] \left[ \begin{array}{c} y \\ a \\ m \end{array} \right]\]

Power iteration

We want to find the page rank value r
Power method: an iterative scheme.
- Support there are N web pages on the Internet.
- All web pages start out with same importance: 1/N.
- Rank vector r: $r^{(0)} = [1/N, …,1/N]^{T}$
- Iterate: $r^{(t+1)} = M\cdot r$
- Stopping condition: $r^{(t+1)} - r^{(t)} < some\ small\ positive\ error\ threshold\ e$.

Example of power iteration

Revisiting our flow equations from Section 2.4:

Iteration 0

Initial rank vector r at iteration 0: $r^{(0)} = [1/3,1/3,1/3]^{T}$

\[\left[ \begin{array}{c} y \\ a \\ m \end{array} \right] = \left[ \begin{array}{c} 1/3 \\ 1/3 \\ 1/3 \end{array} \right]\]

Iteration 1

\[\left[ \begin{array}{c} y \\ a \\ m \end{array} \right] = \left[ \begin{array}{ccc} 1/2 & 1/2 & 0 \\ 1/2 & 0 & 1 \\ 0 & 1/2 & 0 \end{array} \right] \left[ \begin{array}{c} 1/3 \\ 1/3 \\ 1/3 \end{array} \right] = \left[ \begin{array}{c} 1/3 \\ 3/6 \\ 1/6 \end{array} \right]\]

Iteration 2

\[\left[ \begin{array}{c} y \\ a \\ m \end{array} \right] = \left[ \begin{array}{ccc} 1/2 & 1/2 & 0 \\ 1/2 & 0 & 1 \\ 0 & 1/2 & 0 \end{array} \right] \left[ \begin{array}{c} 1/3 \\ 3/6 \\ 1/6 \end{array} \right] = \left[ \begin{array}{c} 5/12 \\ 1/3 \\ 3/12 \end{array} \right]\]

Iteration 3

\[\left[ \begin{array}{c} y \\ a \\ m \end{array} \right] = \left[ \begin{array}{ccc} 1/2 & 1/2 & 0 \\ 1/2 & 0 & 1 \\ 0 & 1/2 & 0 \end{array} \right] \left[ \begin{array}{c} 5/12 \\ 1/3 \\ 3/12 \end{array} \right] = \left[ \begin{array}{c} 9/24 \\ 11/24 \\ 1/6 \end{array} \right]\]

…

Final iteration

\[\left[ \begin{array}{c} y \\ a \\ m \end{array} \right] = \left[ \begin{array}{ccc} 1/2 & 1/2 & 0 \\ 1/2 & 0 & 1 \\ 0 & 1/2 & 0 \end{array} \right] \left[ \begin{array}{c} ... \\ ... \\ ... \end{array} \right] = \left[ \begin{array}{c} 6/15 \\ 6/15 \\ 3/15 \end{array} \right]\]

The final iteration results is the same as the results from the Gaussian approach in Section 2.2

PageRank: the Google formulation

Scenarios

Recalling the equation from section 2.3:

\[r_j = \sum\limits_{i=0}^{N-1} M_{ij}r_{j}\]

Does the above equation converge?
Does it converge to what we want?.
Are the results reasonable?

Does it converge?

The spider trap problem
Build the stochastic adjacency matrix for the following graph and calculate the ranking for a and b.

graph LR
  A((a)) --> B((b));
  B -->A;

Solution

\[\left[ \begin{array}{c} r_a \\ r_b \end{array} \right] = \begin{array}{cccc} 1 & 0 & 1 & 0\\ 0 & 1 & 0 & 1 \end{array}\]

Does it converge to what we want?

The dead end problem
Build the stochastic adjacency matrix for the following: graph and calculate the ranking for a and b.

graph LR
  A((a)) --> B((b));

Solution

\[\left[ \begin{array}{c} r_a \\ r_b \end{array} \right] = \begin{array}{cccc} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 \end{array}\]

The solution: random teleport

At each time step, the random surfer has two options:
- A probably of β to follow an out-going link at random.
- A probably of 1 - β to jump to some random page.
- The value of β are between 0.8 to 0.9.
The surfer will teleport out of the spider trap within a few time steps.
The surfer will definitely teleport out of the dead-end.

flowchart LR
  subgraph ORIGINAL
      direction TB
      Y1((y)) --> Y1((y));
      Y1 --> A1((a));
      A1 --> Y1;
      A1 --> M1((m));
  end
  subgraph RANDOM_TELEPORT
      direction TB
      Y2((y)) --> Y2((y));
      Y2 --> A2((a));
      A2 --> Y2;
      A2 --> M2((m));
      M2 .-> STOP[ ]
  end
  ORIGINAL --> RANDOM_TELEPORT

  style STOP  fill:#FFFFFF00, stroke:#FFFFFF00;

This will change the coefficient matrix from

\[\left[ \begin{array}{ccc} 1/2 & 1/2 & 0 \\ 1/2 & 0 & 0 \\ 0 & 1/2 & 0 \end{array} \right]\]

\[\left[ \begin{array}{ccc} 1/2 & 1/2 & 0 \\ 1/2 & 0 & 1 \\ \textbf{1/3} & \textbf{1/3} & \textbf{1/3} \end{array} \right]\]

Final equation for PageRank:

\[r = \beta M \cdot r + \left[ \frac{1 - \beta}{N}\right]_{N}\]

where $\left[ \frac{1 - \beta}{N}\right]_{N}$ is a vector with all $N$ entries have the same value $\frac{1-\beta}{N}$.

Hands-on: Page Rank in Spark

If you have not already done so, run git pull to update the big-data-engineering repository.

Small example data

A small example data file is located in link-analysis/data/small_graph.dat and has the following format
- Each line represents one link
- On each line, the source (first) and the destination (second) are separated by a single space.

 y y
y a
a y
a m
m a
 

Load and view the data

 graph_data = sc.textFile("small_graph.dat")
graph_data.take(5)
 

Create links

 links = graph_data.map(lambda line: (line.split(" ")[0], line.split(" ")[1]))
print(links.take(10))
links = links.groupByKey()
print(links.take(10))
links = links.mapValues(list)
print(links.take(10))
 

Setup initial weight

 N = links.count()
ranks = links.map(lambda line: (line[0], 1/N))
ranks.take(3)
 

Calculate the votes

 def calculateVotes(t):
    res = []
    for item in t[1][1]:
        count = len(t[1][1])
        res.append((item, t[1][0] / count))
    return res
    ###############################
calculateVotes(('y', (0.3333333333333333, ['y', 'a'])))
 

Vote distribution

 votes = ranks.join(links)
votes.take(10)
 

 votes = votes.flatMap(calculateVotes)
print(votes.take(10))
 

Vote tally

 ranks = votes.reduceByKey(lambda x,y: x + y)
ranks.take(10)
 

Iteration in Spark

 %%time
for i in range(10):
    votes = ranks.join(links).flatMap(calculateVotes)
    ranks = votes.reduceByKey(lambda x,y: x + y)
    print(ranks.take(3))
 

Iterating with a stopping condition

 %%time
sum = 1
while sum > 0.01:
    old_ranks = ranks
    votes = ranks.join(links).flatMap(calculateVotes)
    ranks = votes.reduceByKey(lambda x,y: x + y)
    errors = old_ranks.join(ranks).mapValues(lambda v: abs(v[0] - v[1]))
    sum = errors.values().sum()
    print(sum)
    print(ranks.take(3))
 

Hollins dataset

Download the Hollins dataset using wget
Hollins University web bot crawl in 2004.
Which page is most important (internally).

Hub and Authority

Intuition

PageRank only assume one-dimensional notion of importance
HITS: hyperlink-induced topic search
- Two flavors of importance:
  - Authorities: provide information about a topic
  - Hubs: show where to find out more information about a topic
Example: A typical department at a university maintains a Web page listing all the courses offered by the department, with links to a page for each course.
- If you want to know about a certain course, you need the page for that course (authorities).
- If you want to find out what courses the department is offering, you need the page with the course list first (hub).

Formalizing Hubbiness and Authority

For a collection of pages (enumarated)
- Two vectors: $h$ and $a$
- The $i^{th}$ component of $h$: the hubbiness of the $i^{th}$ page
- The $i^{th}$ component of $a$ gives the authority of the same page.
To compute hubbiness and authority:
- Add the authority of successors to estimate hubbiness
- Add hubbiness of predecessors to estimate authority.
Avoid unlimited growth through scaling:
- Scale the values of the vectors $h$ and $a$ so that the largest component is 1.
- Scale the values of the vectors $h$ and $a$ so that the sum of all components is 1.
Define link matrix of the Web, $L$ and there are n pages on the Web
- $L$ is an n-by-n matrix
  - $L_{ij} = 1$ if there is a link from page i to page j
  - $L_{ij} = 0$ if not.
- $L^T$ is the transpose of $L$
  - $L^T_{ij} = 1$ if there is a link from page j to page i
  - $L^T_{ij} = 0$ if not

Example

Given a 5-node (5 pages) link graph as follows:

flowchart LR
    A((A)) --> B((B))
    A --> C((C))
    A ---> D((D))
    B ---> A
    B ---> D
    C --> E((E))
    D --> B
    D --> C

The $L$ and $L^T$ matrices are:

\[L = \left[ \begin{array}{ccccc} 0 & 1 & 1 & 1 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 & 1 \\ 0 & 1 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 \end{array} \right] \ \ \ \ \ \ \ \ \ L^T = \left[ \begin{array}{ccccc} 0 & 1 & 0 & 0 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 1 & 1 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 \end{array} \right]\]

If indices $0,1,2,3,4$ represent pages $A,B,C,D,E$ accordingly:
- There is a link from B ($i=1$) to D ($j=3$), so $L_{13}=1$
\[\left[ \begin{array}{ccccc} 0 & 1 & 1 & 1 & 0 \\ 1 & 0 & 0 & \textbf{1} & 0 \\ 0 & 0 & 0 & 0 & 1 \\ 0 & 1 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 \end{array} \right]\]
- There is also a link from D ($j=3$) back to B($i=1$), so $L^T_{31}=1$
\[\left[ \begin{array}{ccccc} 0 & 1 & 0 & 0 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 1 & \textbf{1} & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 \end{array} \right]\]
- and so on …

Formal equation

From Section 5.2, for a collection of pages (enumarated)
- Two vectors: $h$ and $a$
- The $i^{th}$ component of $h$: the hubbiness of the $i^{th}$ page
- The $i^{th}$ component of $a$ gives the authority of the same page.
The hubbiness of a page is proportional to the sum of the authority of its successor.
- Explanation: If a hub contains pages of high value (high authority), then the value of that hub (hubbiness) will be high.
- Given $\lambda$ is an unknown scaling factor:
\[h=\lambda La\]
The authority of a page is proportional to the sum of the hubbiness of its predecessor.
- Explanation: If a page is cited by multiple hubs with high value (high hubbiness), then that page must be of high quality (high authority).
- Given $\mu$ is an unknown scaling factor:
\[a=\mu L^{T}h\]
Putting the two equations together:

\[\begin{align} h=\lambda La \\ a=\mu L^{T}h \end{align}\]

Ommiting $\lambda$ and $\mu$ and you can see the same self-calculating structure emerges for $h$ and $a$ similar to how the rank vector $r$ was formalized in Page Rank.
In this case, $h$ and $a$ will alternate their roles, with one being used to calculate the other.

Computation implementation

Construct $L$
Generate $L^T$
Generate $h$ as a vector of all 1’s.
Compute $a = L^{T}h$ then scale so that the largest component is 1
Compute $h = La$ then scale so that the largest component is 1
Repeat the previous two steps until differences of successive values of $h$ and $a$ are small enough.
We demonstrate this procedure using the example from Section 5.3:

Iteration 0

Initialize $h$:

\[h= \left[ \begin{array}{c} 1 \\ 1 \\ 1 \\ 1 \\ 1 \end{array} \right]\]

Compute $a = L^{T}h$

\[a = \left[ \begin{array}{ccccc} 0 & 1 & 0 & 0 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 1 & 1 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 \end{array} \right] \left[ \begin{array}{c} 1 \\ 1 \\ 1 \\ 1 \\ 1 \end{array} \right] = \left[ \begin{array}{c} 1 \\ 2 \\ 2 \\ 2 \\ 1 \end{array} \right]\]

Scale so that the largest component of $a$ is 1
- Since max value in $a$ is 2, we divide all values in $a$ by 2.

\[a= \left[ \begin{array}{c} 1/2 \\ 1 \\ 1 \\ 1 \\ 1/2 \end{array} \right]\]

Compute $h = La$

\[h = \left[ \begin{array}{ccccc} 0 & 1 & 1 & 1 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 & 1 \\ 0 & 1 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 \end{array} \right] \left[ \begin{array}{c} 1/2 \\ 1 \\ 1 \\ 1 \\ 1/2 \end{array} \right] = \left[ \begin{array}{c} 3 \\ 3/2 \\ 1/2 \\ 2 \\ 0 \end{array} \right]\]

Scale so that the largest component of $h$ is 1
- Since max value in $h$ is 3, we divide all values in $h$ by 3.

\[h= \left[ \begin{array}{c} 1 \\ 1/2 \\ 1/6 \\ 2/3 \\ 0 \end{array} \right]\]

Iteration 1

This is $h$ value from previous iteration:

\[h= \left[ \begin{array}{c} 1 \\ 1/2 \\ 1/6 \\ 2/3 \\ 0 \end{array} \right]\]

Compute $a = L^{T}h$

\[a = \left[ \begin{array}{ccccc} 0 & 1 & 0 & 0 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 1 & 1 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 \end{array} \right] \left[ \begin{array}{c} 1 \\ 1/2 \\ 1/6 \\ 2/3 \\ 0 \end{array} \right] = \left[ \begin{array}{c} 1/2 \\ 5/3 \\ 5/3 \\ 3/2 \\ 1/6 \end{array} \right]\]

Scale so that the largest component of $a$ is 1
- Since max value in $a$ is $5/3$, we divide all values in $a$ by $5/3$.

\[a= \left[ \begin{array}{c} 3/10 \\ 1 \\ 1 \\ 9/10 \\ 1/10 \end{array} \right]\]

Compute $h = La$

\[h = \left[ \begin{array}{ccccc} 0 & 1 & 1 & 1 & 0 \\ 1 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 & 1 \\ 0 & 1 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 \end{array} \right] \left[ \begin{array}{c} 3/10 \\ 1 \\ 1 \\ 9/10 \\ 1/10 \end{array} \right] = \left[ \begin{array}{c} 29/10 \\ 6/5 \\ 1/10 \\ 2 \\ 0 \end{array} \right]\]

Scale so that the largest component of $h$ is 1
- Since max value in $h$ is $29/10$, we divide all values in $h$ by $29/10$.

\[a= \left[ \begin{array}{c} 1 \\ 12/29 \\ 1/29 \\ 20/29 \\ 0 \end{array} \right]\]

Final values:

\[\begin{align} h = [1,0.3583,0,0.7165,0] \\ a = [0.2087,1,1,0.7913,0] \end{align}\]

Hands-on: HITS

Example data

Run the following in a cell to generate a data file. This data file represents the graph shown in Section 5.3

 %%file small_web.dat
A B
A D
A C
B A
B D
C E
D C
D B
 

Review the sequential implementation of HITS

Redo Hollins data

Identify the pages with highest level of hubbiness in the Hollins site.