ETC1010: Data Modelling and Computing

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# ETC1010: Data Modelling and Computing
## Lecture 9A: Networks and Graphs
### Dr. Nicholas Tierney & Professor Di Cook
### EBS, Monash U.
### 2019-09-25

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# Announcements

.vlarge[
- Assignment 3 has been released
- NO LECTURE ON FRIDAY
- Project deadlines:
  - **Deadline 3 (11th October) **:  Electronic copy of your data, and a page of data description, and cleaning done, or needing to be done.
  - **Deadline 4 (18th October) **:  Final version of story board uploaded. 
- Practical exam: **18th October in class at 8am**
]

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# recap: Last week on tidy text data

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# Network analysis

# A description of phone calls

.huge[
- Johnny --> Liz
- Liz --> Anna
- Johnny -- > Dan
- Dan --> Liz
- Dan --> Lucy
]

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### As a graph

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# And as an association matrix

.vvvhuge[
[DEMO]
]

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# Nodes and edges?

.huge[
Netword data can be thought of as two related tables, **nodes** and **edges**:

- **nodes** are connection points
- **edges** are the connections between points
]

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# Example: Mad Men. (Nodes = characters from the series)

```
## # A tibble: 45 x 2
##    label          Gender
##    <fct>          <fct> 
##  1 Betty Draper   female
##  2 Don Draper     male  
##  3 Harry Crane    male  
##  4 Joan Holloway  female
##  5 Lane Pryce     male  
##  6 Peggy Olson    female
##  7 Pete Campbell  male  
##  8 Roger Sterling male  
##  9 Sal Romano     male  
## 10 Henry Francis  male  
## # … with 35 more rows
```

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# Example: Mad Men. (Edges = how they are associated)

```
## # A tibble: 39 x 2
##    Name1        Name2           
##    <fct>        <fct>           
##  1 Betty Draper Henry Francis   
##  2 Betty Draper Random guy      
##  3 Don Draper   Allison         
##  4 Don Draper   Bethany Van Nuys
##  5 Don Draper   Betty Draper    
##  6 Don Draper   Bobbie Barrett  
##  7 Don Draper   Candace         
##  8 Don Draper   Doris           
##  9 Don Draper   Faye Miller     
## 10 Don Draper   Joy             
## # … with 29 more rows
```

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# Why care about these relationships?

.huge[
- **Telephone exchanges**: Nodes are the phone numbers. Edges would indicate a call was made betwen two numbers.
- **Book or movie plots**: Nodes are the characters. Edges would indicate whether they appear together in a scene, or chapter. If they speak to each other, various ways we might measure the association.
- **Social media**: nodes would be the people who post on facebook, including comments. Edges would measure who  comments on who's posts.
]

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# Drawing these relationships out:

.vlarge[
One way to describe these relationships is to provide association matrix between many objects. 
]

(Image created by Sam Tyner.) 
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# Example: Madmen

*Source: [wikicommons](https://en.wikipedia.org/wiki/Mad_Men#/media/File:Mad-men-title-card.jpg)*

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# Generate a network view

.huge[
- Create a layout (in 2D) which places nodes which are most related close,
- Plot the nodes as points, connect the appropriate lines
- Overlaying other aspects, e.g. gender
]

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# introducing `tidygraph` and `ggraph`

.pull-left[

```r
library(tidygraph)
library(ggraph)

madmen_graph <-  tbl_graph(
  nodes = madmen$vertices,
  edges = madmen$edges,
  directed = FALSE
  )
```
]

.pull-right[

```
## # A tbl_graph: 45 nodes and 39 edges
## #
## # An undirected simple graph with 6 components
## #
## # Node Data: 45 x 2 (active)
##   label         Gender
##   <fct>         <fct> 
## 1 Betty Draper  female
## 2 Don Draper    male  
## 3 Harry Crane   male  
## 4 Joan Holloway female
## 5 Lane Pryce    male  
## 6 Peggy Olson   female
## # … with 39 more rows
## #
## # Edge Data: 39 x 2
##    from    to
##   <int> <int>
## 1     1     2
## 2     1     3
## 3     4     5
## # … with 36 more rows
```

]

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# plotting using ggraph

```r
gg_madmen <- ggraph(madmen_graph, layout = "kk") + 
  geom_edge_link() + 
  geom_node_label(aes(colour = Gender,
                      label = label))
```

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# plotting using ggraph

```r
gg_madmen
```

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# Which actor was most connected?

```r
madmen_graph %>%
  activate(nodes) %>% 
  mutate(count = centrality_degree()) %>%
  arrange(-count) %>%
  as_tibble()
```

```
## # A tibble: 45 x 3
##    label                    Gender count
##    <fct>                    <fct>  <dbl>
##  1 Joan Holloway            female    14
##  2 Woman at the Clios party female     6
##  3 Janine                   female     5
##  4 Duck Phillips            male       4
##  5 Betty Draper             female     3
##  6 Rachel Menken            female     3
##  7 Hildy                    female     3
##  8 Joy                      female     2
##  9 Vicky                    female     2
## 10 Don Draper               male       1
## # … with 35 more rows
```

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# `activate()` what now?

.pull-left[

```r
madmen_graph
```

.huge.pull-right[
- need to tell dplyr if you are working on `nodes` or `edges`. 
- `activate` means we don't need a `mutate_nodes` or `mutate_edges` commands
]

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# `centrality` what now?

.pull-left[

```r
madmen_graph %>%
  activate(nodes) %>% 
* mutate(count = centrality_degree()) %>%
  arrange(-count) %>%
  as_tibble()
```

.pull-right.vlarge[
- How central is a node or edge in a graph?
- definition is inherently vague
- there are many different centrality scores that exist
- `centrality_degree()` says: "What is the number of adjacent edges?"
]

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# What do we learn?

.huge[
- Joan Holloway had a lot of affairs, all with loyal partners except for his wife Betty, who had two affairs herself
- Followed by Woman at Clios party
]

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# Example: American college football

.huge[
Early American football outfits were like Australian AFL today!
]

*Source: [wikicommons](https://commons.wikimedia.org/wiki/File:Unknown_Early_American_Football_Team.jpg)*

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# Example: American college football

.huge[
Fall 2000 Season of [Division I college football](https://en.wikipedia.org/wiki/NCAA_Division_I). 
- Nodes are the teams, edges are the matches. 
- Teams are broken into "conferences" which are the primary competition, but they can play outside this group.
]

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# Example: American college football

```r
foot_graph
```

```
## # A tbl_graph: 115 nodes and 613 edges
## #
## # A directed acyclic simple graph with 1 component
## #
## # Node Data: 115 x 3 (active)
##   uni          conference     schools
##   <chr>        <chr>          <chr>  
## 1 BrighamYoung Mountain West  <NA>   
## 2 FloridaState Atlantic Coast <NA>   
## 3 Iowa         Big Ten        <NA>   
## 4 KansasState  Big Twelve     <NA>   
## 5 NewMexico    Mountain West  <NA>   
## 6 TexasTech    Big Twelve     <NA>   
## # … with 109 more rows
## #
## # Edge Data: 613 x 3
##    from    to same.conf
##   <int> <int>     <dbl>
## 1     1     2         0
## 2     3     4         0
## 3     1     5         1
## # … with 610 more rows
```

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```r
set.seed(2019-09-25-1117)
gg_foot_graph <-
ggraph(foot_graph, layout = "fr") + 
  geom_edge_link(alpha = 0.2) + 
  geom_node_point(size = 7,
                  alpha = 0.9,
                  aes(colour = conference)) +
  scale_colour_brewer(palette = "Paired") + 
  theme(legend.position = "bottom") 
```

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# What do we learn?

.vlarge[
- Remember layout is done to place nodes that are more similar close together in the display. 
- The colours indicate conference the team belongs too. For the most part, conferences are clustered, more similar to each other than other conferences. 
- There are some clusters of conference groups, eg Mid-American, Big East, and Atlantic Coast
- The Independents are independent
- Some teams play far afield from their conference.
]

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## Example: Harry Potter characters

*Source: [wikicommons](https://commons.wikimedia.org/wiki/File:Harry_Potter_Platform_Kings_Cross.jpg)*

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.huge[
There is a connection between two students if one provides emotional support to the other at some point in the book. 
- Code to pull the data together is provided by Sam Tyner [here](https://github.com/sctyner/geomnet/blob/master/README.Rmd#harry-potter-peer-support-network).
]

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# Harry potter data as nodes and edges

```r
hp
```

```
## # A tbl_graph: 64 nodes and 434 edges
## #
## # A directed multigraph with 29 components
## #
## # Node Data: 64 x 4 (active)
##   name              schoolyear gender house     
##   <chr>                  <dbl> <chr>  <chr>     
## 1 Adrian Pucey            1989 M      Slytherin 
## 2 Alicia Spinnet          1989 F      Gryffindor
## 3 Angelina Johnson        1989 F      Gryffindor
## 4 Anthony Goldstein       1991 M      Ravenclaw 
## 5 Blaise Zabini           1991 M      Slytherin 
## 6 C. Warrington           1989 M      Slytherin 
## # … with 58 more rows
## #
## # Edge Data: 434 x 3
##    from    to  book
##   <int> <int> <dbl>
## 1    11    25     1
## 2    11    26     1
## 3    11    44     1
## # … with 431 more rows
```

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# Let's plot the characters

```r
ggraph_hp <- 
ggraph(hp,
       layout = "fr") + 
  geom_edge_link(alpha = 0.2) + 
  geom_node_point(aes(colour = house,
                      shape = gender)) + 
  # geom_node_text(aes(label = name)) +
  facet_edges(~book,
              ncol = 2) +
  scale_colour_manual(values = c("#941B08","#F1F31C", 
                                 "#071A80", "#154C07"))
```

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# Let's plot the characters

```r
ggraph_hp
```

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# Your turn: rstudio.cloud

.vlarge.pull-left[
- Read in last semesters class data, which contains `s1_name` and `s2_name` are the first names of class members, and tutors, with the latter being the "go-to" person for the former. 
- Write the code to produce a class network that looks something like below
]

.pull-right[
![](images/class_network.png)
]

<!--
## Simpsons

Your turn to make a network diagram for the Simpsons. The measure of association will be "that the two characters had lines in the same episode together".

- How many characters appeared only in one episode? (You will want to drop these)
- Write code to search if a character has a line in an episode
- Compile a dataset of episode (rows) and character (columns) which is a binary matrix where 1 indicates the character had a line in the episode, and 0 is otherwise
- Gather the matrix into long form, with these columns: `episode`, `character`, `had a line` (0,1)
- Filter the rows with `had a line` equal to 1. 
- Count the number of times the pair of characters appeared. This now forms your edge set, with an additional column of the strength of the relationship
- Make your network display
-->

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