elias-hanna_community_detection_NetLogo/mini_projet_community_detection.nlogo
;;;;;;;;;;;;;;;;;;;;;;;;
;;; Setup Procedures ;;;
;;;;;;;;;;;;;;;;;;;;;;;;
turtles-own [age religion classe politique langue tweet retweets]

to setup
  clear-all
  set-default-shape turtles "circle"
  ;; make the initial network of two turtles and an edge
  init_network
  ;;make-node nobody        ;; first node, unattached
  ;;make-node turtle 0      ;; second node, attached to first node
  reset-ticks
end

;;;;;;;;;;;;;;;;;;;;;;;
;;; Main Procedures ;;;
;;;;;;;;;;;;;;;;;;;;;;;

to go
  ;; All the turtles tweet
  ask turtles [ send-tweet ]
  ;; Turtles process the tweets from this turn and retweets from last turn
  ask turtles [ process-tweets ]
  show-community detect-community
  ;ask links [ set color gray ]
  tick
  ;;if layout? [ layout ]
end

;; Create the network
to init_network
  create-turtles agent_number
  [
    set color 9.9
    set age ((random-float 87) + 13) ;; Age entre 13 et 100 ans (exclu)
    set religion (random 4) ;; 4 religions differentes (0, 1, 2, 3)
    set classe (random-float 100) ;; Classe economique de 0 a 100% (exclu) ex: 10% -> parmi les 10% les plus riches, 90% -> parmi les 90% les plus riches <-> 10% les plus pauvres
    set politique (random-float 4) ;; Choix politique de 0 a 4 (exclu): 0 extreme gauche, 1 gauche, 2 centre, 3 droite,  4 extreme droite (entre deux possibles)
    set langue []
    repeat ((random 3) + 1) ;; Uniformely, people can have between 1 and 3 languages they speak
    [
      set langue (lput (random 10) langue) ;; 10 different languages
    ]
    setxy random-xcor random-ycor
  ]
end

;; Initialize the links between the nodes
;; At the beginning, all the nodes are processed in order to find the 5 that are most linked to the current node according to its beliefs and create links with those
to process-tweets
  ;; Create a list without the current turtle
  let others (turtles with [self != myself])
  let tmp_retweets []
  ask others
  [
    ;; randomly sees the tweet depending on wether or not agent follows other agetn
    ifelse((link-with myself) != nobody)
    [
      ;; 80% chance to see the tweet/retweet
      if(random 100 < 80)
      [
        ;; Check the ressemblance between the tweet and the agent that called ask others
        ;; If the tweet ressemble the agent at more than 70% then it retweets it
        if((ressemblance-tweet tweet myself) > 70)
        [
          ;; we add the provenance of the tweet to the retweet
          set (tmp_retweets) (lput (lput self tweet) tmp_retweets)
        ]
        ;; There is a link but the tweet is so bad that we unfollow
        if((ressemblance-tweet tweet myself) < 50)
        [
          ask link-with myself [ die ]
        ]
      ]
      if(random 100 < 80 and not empty? retweets)
      [
        ;; Check the ressemblance between the retweet and the agent that called ask others
        ;; If the tweet ressemble the agent at more than 70% then it retweets it
        let chosen_retweet (one-of retweets)
        if((ressemblance-tweet chosen_retweet myself) > 70)
        [
          set (tmp_retweets) (lput chosen_retweet tmp_retweets)
        ]
        if((ressemblance-tweet chosen_retweet myself) > 80 and (item 5 chosen_retweet) != myself)
        [
          ask myself[ create-link-with (item 5 chosen_retweet)]
          set (tmp_retweets) (lput chosen_retweet tmp_retweets)
        ]
        ;; There is a link but the tweet is so bad that we unfollow
        if((ressemblance-tweet chosen_retweet myself) < 50)
        [
          ask myself [ if((link-with (item 5 chosen_retweet)) != nobody) [ ask link-with (item 5 chosen_retweet) [die] ]]
        ]
      ]
    ]
    ;;Case where agent does not follow
    [
      ;; 20% chance to see the tweet
      if(random 100 < 20)
      [
        ;; Check the ressemblance between the tweet and the agent that called ask others
        ;; If the tweet ressemble the agent at more than 70% then it retweets it
        if((ressemblance-tweet tweet myself) > 70)
        [
          set (tmp_retweets) (lput (lput self tweet) tmp_retweets)
        ]
        ;; There is no link but the tweet is so good that we follow
        if((ressemblance-tweet tweet myself) > 80)
        [
          create-link-with myself
        ]
      ]
      ;; 20% chance to see one of the retweets
      if(random 100 < 20 and not empty? retweets)
      [
        ;; Check the ressemblance between the retweet and the agent that called ask others
        ;; If the tweet ressemble the agent at more than 70% then it retweets it
        let chosen_retweet (one-of retweets)
        if((ressemblance-tweet chosen_retweet myself) > 70)
        [
          set (tmp_retweets) (lput chosen_retweet tmp_retweets)
        ]
        if((ressemblance-tweet chosen_retweet myself) > 80 and (item 5 chosen_retweet) != myself)
        [
          ask myself[ create-link-with (item 5 chosen_retweet)]
          set (tmp_retweets) (lput chosen_retweet tmp_retweets)
        ]
        ;; There is a link but the tweet is so bad that we unfollow
        if((ressemblance-tweet chosen_retweet myself) < 50)
        [
          ask myself [ if((link-with (item 5 chosen_retweet)) != nobody) [ ask link-with (item 5 chosen_retweet) [die] ]]
        ]
      ]
    ]
  ]

  set retweets tmp_retweets
end

to send-tweet
  let tweet_age (seuil (random-normal age 15) 13 100)
  let tweet_religion religion
  let tweet_classe (seuil (random-normal classe 30) 0 100)
  let tweet_politique (seuil (random-normal politique 1) 0 4)
  let tweet_langue one-of langue
  set tweet (list tweet_age tweet_religion tweet_classe tweet_politique tweet_langue)
  if(retweets = 0) [set retweets []]
end

to-report seuil[nb minimum maximum]
  if(nb < minimum) [ report minimum]
  if(nb > maximum) [ report maximum]
  report nb
end

;; Calcul de la ressemblance entre 2 noeuds
to-report ressemblance [node]
  let ret 0
  ;; Age
  set ret (ret + (100 - 100 * abs(age - [age] of node) / 87))
  ;; Religion
  set ret (ret + (same-religion node) * 100)
  ;; Classe
  set ret (ret + (100 - 100 * abs(classe - [classe] of node) / 100))
  ;; Politique
  set ret (ret + (100 - 100 * abs(politique - [politique] of node) / 4))
  ;; Langue
  set ret (ret + (same-language node) * 100)
  report (ret / 5)
end

to-report ressemblance-tweet [l n]
  let ret 0
  ;; Age
  set ret (ret + (100 - 100 * abs((item 0 l) - [age] of n) / 87))
  ;; Religion
  set ret (ret + (same-rel-list (item 1 l) n) * 100)
  ;; Classe
  set ret (ret + (100 - 100 * abs((item 2 l) - [classe] of n) / 100))
  ;; Politique
  set ret (ret + (100 - 100 * abs((item 3 l) - [politique] of n) / 4))
  ;; Langue
  set ret (ret + (same-lan-list (item 4 l) n) * 100)
  report (ret / 5)
end

;; Same language spoke
to-report same-lan-list [l n]
  foreach [langue] of n
  [
    t ->
    if (l = t) [ report 1 ]
  ]
  report 0
end

to-report same-rel-list[r n]
  ifelse (r != [religion] of n)
  [
    report 0
  ]
  [
    report 1
  ]
end

;; Same religion or not
to-report same-religion [node]
  ifelse (religion != [religion] of node)
  [
    report 0
  ]
  [
    report 1
  ]
end

;; Same language spoke
to-report same-language [node]
  foreach langue
  [
    l ->
    if (member? l ([langue] of node)) [ report 1 ]
  ]
  report 0
end
;; used for creating a new node
to make-node [old-node]
  create-turtles 1
  [
    set color red
    if old-node != nobody
      [ create-link-with old-node [ set color green ]
        ;; position the new node near its partner
        move-to old-node
        fd 8
      ]
  ]
end

to-report detect-community
  let nkernel 3
  let kernels (list)

  ;;retrieve list of turtles with most links
  repeat nkernel
  [
    let chosen_turtle nobody
    let chosen_nlink 0
    ask turtles [
      let nlink count my-links
      if (nlink >= chosen_nlink) and (not member? self kernels)
      [
       set chosen_turtle self
       set chosen_nlink nlink
      ]
    ]
    set kernels lput chosen_turtle kernels
  ]

  ;;classify each nodes
  let partition []
  repeat nkernel
  [
    set partition (lput [] partition)
  ]
  ask turtles
  [
    let min_kernel nobody
    let min_val 10000
    let min_i 0
    let i 0
    show kernels
    foreach kernels ;;find nearest kernel for each turtle
    [
      k ->
      let dist ressemblance(k)
      if (dist < min_val)
      [
        set min_val dist
        set min_kernel k
        set min_i i
      ]
      set i (i + 1)
    ]

    set (partition) (replace-item (min_i) partition (lput self (item (min_i) partition)))
  ]

  report partition
end

to show-community [partition]

  let node_color 5
  let i 0
  show partition
  foreach partition
  [
    part ->
    foreach part
    [
      turt ->
      ask turt
      [
        set color (node_color + i * 10)
      ]
    ]

    set i (i + 1)
  ]
end

;;;;;;;;;;;;;;
;;; Layout ;;;
;;;;;;;;;;;;;;

;; resize-nodes, change back and forth from size based on degree to a size of 1
to resize-nodes
  ifelse all? turtles [size <= 1]
  [
    ;; a node is a circle with diameter determined by
    ;; the SIZE variable; using SQRT makes the circle's
    ;; area proportional to its degree
    ask turtles [ set size sqrt count link-neighbors ]
  ]
  [
    ask turtles [ set size 1 ]
  ]
end

to layout
  ;; the number 3 here is arbitrary; more repetitions slows down the
  ;; model, but too few gives poor layouts
  repeat 3 [
    ;; the more turtles we have to fit into the same amount of space,
    ;; the smaller the inputs to layout-spring we'll need to use
    let factor sqrt count turtles
    ;; numbers here are arbitrarily chosen for pleasing appearance
    layout-spring turtles links (1 / factor) (7 / factor) (1 / factor)
    display  ;; for smooth animation
  ]
  ;; don't bump the edges of the world
  let x-offset max [xcor] of turtles + min [xcor] of turtles
  let y-offset max [ycor] of turtles + min [ycor] of turtles
  ;; big jumps look funny, so only adjust a little each time
  set x-offset limit-magnitude x-offset 0.1
  set y-offset limit-magnitude y-offset 0.1
  ask turtles [ setxy (xcor - x-offset / 2) (ycor - y-offset / 2) ]
end

to-report limit-magnitude [number limit]
  if number > limit [ report limit ]
  if number < (- limit) [ report (- limit) ]
  report number
end

to show_hide_links
  ifelse ([hidden?] of one-of links)
  [
    ask links [ show-link ]
  ]
  [
    ask links [ hide-link ]
  ]
end

; Copyright 2005 Uri Wilensky.
; See Info tab for full copyright and license.
@#$#@#$#@
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@#$#@#$#@
## WHAT IS IT?

In some networks, a few "hubs" have lots of connections, while everybody else only has a few.  This model shows one way such networks can arise.

Such networks can be found in a surprisingly large range of real world situations, ranging from the connections between websites to the collaborations between actors.

This model generates these networks by a process of "preferential attachment", in which new network members prefer to make a connection to the more popular existing members.

## HOW IT WORKS

The model starts with two nodes connected by an edge.

At each step, a new node is added.  A new node picks an existing node to connect to randomly, but with some bias.  More specifically, a node's chance of being selected is directly proportional to the number of connections it already has, or its "degree." This is the mechanism which is called "preferential attachment."

## HOW TO USE IT

Pressing the GO ONCE button adds one new node.  To continuously add nodes, press GO.

The LAYOUT? switch controls whether or not the layout procedure is run.  This procedure attempts to move the nodes around to make the structure of the network easier to see.

The PLOT? switch turns off the plots which speeds up the model.

The RESIZE-NODES button will make all of the nodes take on a size representative of their degree distribution.  If you press it again the nodes will return to equal size.

If you want the model to run faster, you can turn off the LAYOUT? and PLOT? switches and/or freeze the view (using the on/off button in the control strip over the view). The LAYOUT? switch has the greatest effect on the speed of the model.

If you have LAYOUT? switched off, and then want the network to have a more appealing layout, press the REDO-LAYOUT button which will run the layout-step procedure until you press the button again. You can press REDO-LAYOUT at any time even if you had LAYOUT? switched on and it will try to make the network easier to see.

## THINGS TO NOTICE

The networks that result from running this model are often called "scale-free" or "power law" networks. These are networks in which the distribution of the number of connections of each node is not a normal distribution --- instead it follows what is a called a power law distribution.  Power law distributions are different from normal distributions in that they do not have a peak at the average, and they are more likely to contain extreme values (see Albert & Barabási 2002 for a further description of the frequency and significance of scale-free networks).  Barabási and Albert originally described this mechanism for creating networks, but there are other mechanisms of creating scale-free networks and so the networks created by the mechanism implemented in this model are referred to as Barabási scale-free networks.

You can see the degree distribution of the network in this model by looking at the plots. The top plot is a histogram of the degree of each node.  The bottom plot shows the same data, but both axes are on a logarithmic scale.  When degree distribution follows a power law, it appears as a straight line on the log-log plot.  One simple way to think about power laws is that if there is one node with a degree distribution of 1000, then there will be ten nodes with a degree distribution of 100, and 100 nodes with a degree distribution of 10.

## THINGS TO TRY

Let the model run a little while.  How many nodes are "hubs", that is, have many connections?  How many have only a few?  Does some low degree node ever become a hub?  How often?

Turn off the LAYOUT? switch and freeze the view to speed up the model, then allow a large network to form.  What is the shape of the histogram in the top plot?  What do you see in log-log plot? Notice that the log-log plot is only a straight line for a limited range of values.  Why is this?  Does the degree to which the log-log plot resembles a straight line grow as you add more nodes to the network?

## EXTENDING THE MODEL

Assign an additional attribute to each node.  Make the probability of attachment depend on this new attribute as well as on degree.  (A bias slider could control how much the attribute influences the decision.)

Can the layout algorithm be improved?  Perhaps nodes from different hubs could repel each other more strongly than nodes from the same hub, in order to encourage the hubs to be physically separate in the layout.

## NETWORK CONCEPTS

There are many ways to graphically display networks.  This model uses a common "spring" method where the movement of a node at each time step is the net result of "spring" forces that pulls connected nodes together and repulsion forces that push all the nodes away from each other.  This code is in the `layout-step` procedure. You can force this code to execute any time by pressing the REDO LAYOUT button, and pressing it again when you are happy with the layout.

## NETLOGO FEATURES

Nodes are turtle agents and edges are link agents. The model uses the ONE-OF primitive to chose a random link and the BOTH-ENDS primitive to select the two nodes attached to that link.

The `layout-spring` primitive places the nodes, as if the edges are springs and the nodes are repelling each other.

Though it is not used in this model, there exists a network extension for NetLogo that comes bundled with NetLogo, that has many more network primitives.

## RELATED MODELS

See other models in the Networks section of the Models Library, such as Giant Component.

See also Network Example, in the Code Examples section.

## CREDITS AND REFERENCES

This model is based on:
Albert-László Barabási. Linked: The New Science of Networks, Perseus Publishing, Cambridge, Massachusetts, pages 79-92.

For a more technical treatment, see:
Albert-László Barabási & Reka Albert. Emergence of Scaling in Random Networks, Science, Vol 286, Issue 5439, 15 October 1999, pages 509-512.

Barabási's webpage has additional information at: http://www.barabasi.com/

The layout algorithm is based on the Fruchterman-Reingold layout algorithm.  More information about this algorithm can be obtained at: https://cs.brown.edu/~rt/gdhandbook/chapters/force-directed.pdf.

For a model similar to the one described in the first suggested extension, please consult:
W. Brian Arthur, "Urban Systems and Historical Path-Dependence", Chapt. 4 in Urban systems and Infrastructure, J. Ausubel and R. Herman (eds.), National Academy of Sciences, Washington, D.C., 1988.

## HOW TO CITE

If you mention this model or the NetLogo software in a publication, we ask that you include the citations below.

For the model itself:

* Wilensky, U. (2005).  NetLogo Preferential Attachment model.  http://ccl.northwestern.edu/netlogo/models/PreferentialAttachment.  Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Please cite the NetLogo software as:

* Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

## COPYRIGHT AND LICENSE

Copyright 2005 Uri Wilensky.

![CC BY-NC-SA 3.0](http://ccl.northwestern.edu/images/creativecommons/byncsa.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.  To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at uri@northwestern.edu.

<!-- 2005 -->
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Polygon -7500403 true true 151 1 185 108 298 108 207 175 242 282 151 216 59 282 94 175 3 108 116 108

target
false
0
Circle -7500403 true true 0 0 300
Circle -16777216 true false 30 30 240
Circle -7500403 true true 60 60 180
Circle -16777216 true false 90 90 120
Circle -7500403 true true 120 120 60

tree
false
0
Circle -7500403 true true 118 3 94
Rectangle -6459832 true false 120 195 180 300
Circle -7500403 true true 65 21 108
Circle -7500403 true true 116 41 127
Circle -7500403 true true 45 90 120
Circle -7500403 true true 104 74 152

triangle
false
0
Polygon -7500403 true true 150 30 15 255 285 255

triangle 2
false
0
Polygon -7500403 true true 150 30 15 255 285 255
Polygon -16777216 true false 151 99 225 223 75 224

truck
false
0
Rectangle -7500403 true true 4 45 195 187
Polygon -7500403 true true 296 193 296 150 259 134 244 104 208 104 207 194
Rectangle -1 true false 195 60 195 105
Polygon -16777216 true false 238 112 252 141 219 141 218 112
Circle -16777216 true false 234 174 42
Rectangle -7500403 true true 181 185 214 194
Circle -16777216 true false 144 174 42
Circle -16777216 true false 24 174 42
Circle -7500403 false true 24 174 42
Circle -7500403 false true 144 174 42
Circle -7500403 false true 234 174 42

turtle
true
0
Polygon -10899396 true false 215 204 240 233 246 254 228 266 215 252 193 210
Polygon -10899396 true false 195 90 225 75 245 75 260 89 269 108 261 124 240 105 225 105 210 105
Polygon -10899396 true false 105 90 75 75 55 75 40 89 31 108 39 124 60 105 75 105 90 105
Polygon -10899396 true false 132 85 134 64 107 51 108 17 150 2 192 18 192 52 169 65 172 87
Polygon -10899396 true false 85 204 60 233 54 254 72 266 85 252 107 210
Polygon -7500403 true true 119 75 179 75 209 101 224 135 220 225 175 261 128 261 81 224 74 135 88 99

wheel
false
0
Circle -7500403 true true 3 3 294
Circle -16777216 true false 30 30 240
Line -7500403 true 150 285 150 15
Line -7500403 true 15 150 285 150
Circle -7500403 true true 120 120 60
Line -7500403 true 216 40 79 269
Line -7500403 true 40 84 269 221
Line -7500403 true 40 216 269 79
Line -7500403 true 84 40 221 269

x
false
0
Polygon -7500403 true true 270 75 225 30 30 225 75 270
Polygon -7500403 true true 30 75 75 30 270 225 225 270
@#$#@#$#@
NetLogo 6.0.4
@#$#@#$#@
set layout? false
set plot? false
setup repeat 300 [ go ]
repeat 100 [ layout ]
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
default
0.0
-0.2 0 0.0 1.0
0.0 1 1.0 0.0
0.2 0 0.0 1.0
link direction
true
0
Line -7500403 true 150 150 90 180
Line -7500403 true 150 150 210 180
@#$#@#$#@
0
@#$#@#$#@
