This is done in order to prevent the model from ending up in an infinite while-loop in the WIRE3 procedure. The maximum value for the NUM-LINKS slider is dynamic. Nodes are turtle agents and edges are link agents.
RANDOM NETLOGO CODE
See also Network Example, in the Code Examples section of the Models Library. See other models in the Networks section of the Models Library, such as Preferential Attachment. Are they structurally similar? If not, how are they different? RELATED MODELS Use the bundled NetLogo network extension and run some basic network metrics on the various random networks. The #LINKS monitor shows the total number of links in the network.įor each of the four wiring methods, what kind of structure does the resulting network have?ĭoes each method produce the same number of links each time? How do the MIN-DEGREE and MAX-DEGREE vary across the wiring methods? THINGS TO TRYįor the two variants of the Erdős–Rényi random network, do the min and max degrees follow the number of links proportionally? Can you think of why or why not? EXTENDING THE MODEL MIN-DEG shows the degree of the node with the least links. MAX-DEG shows the degree of the node with the most links.
For a variant of the Erdős–Rényi random network that has a fixed probability for each pair of nodes to have a link, you can set the WIRING-PROB slider and then press WIRE4. Pressing any of the WIRE buttons will create random networks using the different algorithms.įor a classic Erdős–Rényi random network, set the NUM-LINKS slider and then press WIRE3 to create that number of links in your network. For each pair of turtles, creates a link between them wth probability WIRING-PROB. WIRE4: A variant of the classic Erdős–Rényi random network. WIRE3: The classic Erdős–Rényi random network. WIRE2: Pick a random turtle and ask it to try to link to another random turtle. WIRE1: Each turtle asks one other turtle to link to it. There are four different wiring methods illustrated: SETUP creates NUM-NODES turtles and gives them a random location in the world. This model shows four different ways to create random networks, 2 of which create the variants of the classic Erdős–Rényi networks. The most well known are two variants called Erdős–Rényi networks. There are several different models for random networks. The theory of random networks was first introduced by mathematicians Paul Erdős and Alfréd Rényi in 1959. The model, as well as any updates to the model, can also be found on the textbook website. This model is in the IABM Textbook folder of the NetLogo Models Library. Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo. This model is from Chapter Five of the book "Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo", by Uri Wilensky & William Rand. You can also Try running it in NetLogo Web
RANDOM NETLOGO DOWNLOAD
If you download the NetLogo application, this model is included. number with a mean of 10.Beginners Interactive NetLogo Dictionary (BIND) prints a normally distributed random floating point
prints an exponentially distributed random floating Random-poisson reports a Poisson-distributed random integer. Random-normal reports a normally distributed random floating point number. (Note: for results with a given mean and variance, use inputs as follows: alpha = mean * mean / variance lambda = 1 / (variance / mean).) Random-gamma reports a gamma-distributed random floating point number as controlled by the floating point alpha and lambda parameters. It is equivalent to (- mean) * ln random-float 1.0. Random-exponential reports an exponentially distributed random floating point number. (The standard deviation may not be negative.) Reports an accordingly distributed random number with the mean and, in the case of the normal distribution, the standard-deviation.
Random-exponential 1.2.1 random-gamma 2.0 random-normal 1.2.1 random-poisson 1.2.1 random-exponential mean random-gamma alpha lambda random-normal mean standard-deviation random-poisson mean
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