NetLogo Nw Extension

Usage

The first thing that one needs to understand in order to work with the network extension is how to tell the extension which network to work with. Consider the following example situation:

breed [ bankers banker ]
breed [ clients client ]

undirected-link-breed [ friendships friendship ]
directed-link-breed [ accounts account ]

Basically, you have bankers and clients. Clients can have accounts with bankers. Bankers can probably have account with other bankers, and anyone can be friends with anyone.

Now we might want to consider this whole thing as one big network. If that is the case, there is nothing special to do: by default, the NW extension primitives consider all turtles and all links to be part of the current network.

We could also, however, be only interested in a subset of the network. Maybe we want to consider only friendship relations. Furthermore, maybe we want to consider only the friendships between bankers. After all, having a very high centrality in a network of banker friendships is very different from having a high centrality in a network of client friendships.

To specify such networks, we need to tell the extension both which turtles and which links we are interested in. All the turtles from the specified set of turtles will be included in the network, and only the links from the specified set of links that are between turtles of the specified set will be included. For example, if you ask for bankers and friendships, even the lonely bankers with no friends will be included, but friendship links between bankers and clients will not be included. The way to tell the extension about this is with the nw:set-context primitive, which you must call prior to doing any operations on a network.

Some examples:

Special agentsets vs normal agentsets

It must be noted that NetLogo has two types of agentsets that behave slightly differently, and that this has an impact on the way nw:set-context works. We will say a few words about these concepts here but, for a thorough understanding, it is highly recommended that you read the section on agentsets in the NetLogo programming guide.

The “special” agentsets in NetLogo are turtles, links and the different “breed” agentsets. What is special about them is that they can grow: if you create a new turtle, it will be added to the turtles agentset. If you have a bankers breed and you create a new banker, it will be added to the bankers agentset and to the turtles agentset. Same goes for links. Other agentsets, such as those created with the with primitive (e.g., turtles with [ color = red ]) or the turtle-set and link-set primitives) are never added to. The content of normal agentsets will only change if the agents that they contain die.

To show how different types of agentsets interact with nw:set-context, let’s create a very simple network:

Let’s set the context to turtles and links (which is the default anyway) and use nw:get-context to see what we have:

We get all three turtles and all three links:

Now let’s kill one turtle:

As expected, the context is updated to reflect the death of the turtle and of the two links that died with it:

What if we now create a new turtle?

Since our context is using the special turtles agentset, the new turtle is automatically added:

Now let’s demonstrate how it works with normal agentsets. We start over with a new network of red turtles:

And we set the context to turtles with [ color = red ]) and links

Since all turtles are red, we get everything in our context:

But what if we ask one of them to turn blue?

No change. The agentset used in our context remains unaffected:

If we kill one of them, however…

It gets removed from the set:

What if we add a new red turtle?

Nope:

A note regarding floating point calculations

Neither JGraphT nor Jung, the two network libraries that we use internally, use strictfp floating point calculations. This does mean that exact reproducibility of results involving floating point calculations between different hardware architectures is not fully guaranteed. (NetLogo itself always uses strict math so this only applies to some primitives of the NW extension.)

Performance

In order to be fast in as many circumstances as possible, the NW extension tries hard to never calculate things twice. It remembers all paths, distances, and centralities that it calculates. So, while the first time you ask for the distance between turtle 0 and turtle 3782 may take some time, after that, it should be almost instantaneous. Furthermore, it keeps track of values it just happened to calculate along the way. For example, if turtle 297 is closer to turtle 0 than turtle 3782 is, it may just happen to figure out the distance between turtle 0 and turtle 297 while it figures out the distance between turtle 0 and turtle 3782. It will remember this value, so that if you ask it for the distance between turtle 0 and turtle 297, it doesn’t have to do all that work again.

There are a few circumstances where the NW extension has to forget things. If the network changes at all (you add turtles or links, or remove turtles or links), it has to forget everything. For weighted primitives, if the value of the weight variable changes for any of the links in the network, it will forget the values associated with that weight variable.

If you’re working on a network that can change regularly, try to do all your network calculations at once, then all your network changes at once. The more your interweave network calculations and network changes, the more the NW extension will have to recalculate things. For example, if you have a traffic model, and cars need to figure out the shortest path to their destination based on the traffic each tick, have all the cars find their shortest paths, then change the network weights to account for how traffic has changed.

There may be rare occasions in which you don’t want the NW extension to remember values. For example, if you’re working on an extremely large network, remembering all those values may take more memory than you have. In that case, you can just call nw:set-context (first nw:get-context) (last nw:get-context) to force the NW extension to immediately forget everything.

Primitives

Generators

nw:generate-preferential-attachment nw:generate-random nw:generate-watts-strogatz nw:generate-small-world nw:generate-lattice-2d nw:generate-ring nw:generate-star nw:generate-wheel

Path and Distance

nw:turtles-in-radius nw:turtles-in-reverse-radius nw:distance-to nw:weighted-distance-to nw:path-to nw:turtles-on-path-to nw:weighted-path-to nw:turtles-on-weighted-path-to nw:mean-path-length nw:mean-weighted-path-length

Clusterer/Community Detection

nw:bicomponent-clusters nw:weak-component-clusters nw:louvain-communities nw:maximal-cliques nw:biggest-maximal-cliques

Context Management

nw:set-context nw:get-context nw:with-context

Import and Export

nw:save-matrix nw:load-matrix nw:save-graphml nw:load-graphml nw:load nw:save

Centrality Measures

nw:betweenness-centrality nw:eigenvector-centrality nw:page-rank nw:closeness-centrality nw:weighted-closeness-centrality

Clustering Measures

nw:clustering-coefficient nw:modularity

nw:set-context

nw:set-context turtleset linkset

Specifies the set of turtles and the set of links that the extension will consider to be the current graph. All the turtles from turtleset and all the links from linkset that connect two turtles from turtleset will be included.

This context is used by all other primitives (unless specified otherwise) until a new context is specified. (At the moment, only the generator primitives and the file input primitives are exceptions to this rule.)

See the usage section for a much more detailed explanation of nw:set-context.

nw:get-context

nw:get-context

Reports the content of the current graph context as a list containing two agentsets: the agentset of turtles that are part of the context and the agentset of links that are part of the context.

Let’s say we start with a blank slate and the default context consisting of turtles and links, nw:get-context will report a list the special turtles and links breed agentsets:

If we add some turtles and links to our context, we’ll still see the same thing, even though turtles and links have internally grown:

If you had set your context to normal agentsets instead (built with turtle-set, link-set or with) here is what you would see:

If you then create new turtles and links, they are not added to the context because normal agentsets don’t grow (see Special agentsets vs normal agentsets):

But if you construct new agentsets and set the context to them, your new agents will be there:

If you want to see the actual content of your context, it is easy to turn your agentsets into lists that can be nicely displayed. Just use a combination of map and sort:

Finally, you can use nw:get-context to store a context that you eventually want to restore:

Here is the result:

nw:with-context

nw:with-context turtleset linkset command-block

Executes the command-block with the context temporarily set to turtleset and linkset. After command-block finishes running, the previous context will be restored.

For example:

If you have NW extension code running in two forever buttons or loop blocks that each need to use different contexts, you should use nw:with-context in each to make sure they are operating in the correct context.

nw:turtles-in-radius

nw:turtles-in-radius radius

Returns the set of turtles within the given distance (number of links followed) of the calling turtle in the current context, including the calling turtle.

nw:turtles-in-radius form will follow both undirected links and directed out links. You can think of turtles-in-radius as “turtles who I can get to in radius steps”.

If you want the primitive to follow only undirected links or only directed links, you can do it by setting the context appropriately. For example: nw:set-context turtles undir-links (assuming undir-links is an undirected link breed) or nw:set-context turtles dir-links (assuming dir-links is a directed link breed).

Example:

Will output:

As you may have noticed, the result includes the calling turtle. This mimics the behavior of the regular NetLogo in-radius primitive.

nw:turtles-in-reverse-radius

nw:turtles-in-reverse-radius radius

Like nw:turtles-in-radius, but follows in-links instead of out-links. Also follow undirected links. You can think of turtles-in-reverse-radius as “turtles who can get to me in radius steps”.

nw:distance-to

nw:distance-to target-turtle

Finds the shortest path to the target turtle and reports the total distance for this path, or false if no path exists in the current context. Each link counts for a distance of one.

Example:

Will output:

nw:weighted-distance-to

nw:weighted-distance-to target-turtle weight-variable

Like nw:distance-to, but takes link weight into account. The weights cannot be negative numbers.

Example:

Will output:

nw:path-to

nw:path-to target-turtle

Finds the shortest path to the target turtle and reports the actual path between the source and the target turtle. The path is reported as the list of links that constitute the path.

If no path exist between the source and the target turtles, false will be reported instead.

Note that the NW-Extension remembers paths that its calculated previously unless the network changes. Thus, you don’t need to store paths to efficiently move across the network; you can just keep re-calling one of the path primitives. If the network changes, however, the stored answers are forgotten. Example:

Will output:

nw:turtles-on-path-to

nw:turtles-on-path-to target-turtle

Like nw:path-to, but the turtles on the path are reported, instead of the links, including the source turtle and target turtle.

Example:

Will output:

nw:weighted-path-to

nw:weighted-path-to target-turtle weight-variable

Like nw:path-to, but takes link weight into account.

Example:

Will output:

nw:turtles-on-weighted-path-to

nw:turtles-on-weighted-path-to target-turtle weight-variable

Like nw:turtles-on-path-to, but takes link weight into account.

Example:

Will output:

nw:mean-path-length

nw:mean-path-length

Reports the average shortest-path length between all distinct pairs of nodes in the current context.

Reports false unless paths exist between all pairs.

Example:

Will ouput:

nw:mean-weighted-path-length

nw:mean-weighted-path-length weight-variable

Like nw:mean-path-length, but takes into account link weights.

Example:

Will ouput:

nw:betweenness-centrality

nw:betweenness-centrality

To calculate the betweenness centrality of a turtle, you take every other possible pairs of turtles and, for each pair, you calculate the proportion of shortest paths between members of the pair that passes through the current turtle. The betweenness centrality of a turtle is the sum of these.

As of now, link weights are not taken into account.

nw:eigenvector-centrality

nw:eigenvector-centrality

The Eigenvector centrality of a node can be thought of as the amount of influence a node has on a network. In practice, turtles that are connected to a lot of other turtles that are themselves well-connected (and so on) get a higher Eigenvector centrality score.

In this implementation, the eigenvector centrality is normalized such that the highest eigenvector centrality a node can have is 1. This implementation is designed to agree with Gephi’s implementation out to at least 3 decimal places. If you discover that it disagrees with Gephi on a particular network, please report it.

The primitive respects link direction, even in mixed-directed networks. This is the one place where it should disagree with Gephi; Gephi refuses to treat directed links as directed in mixed-networks.

As of now, link weights are not taken into account.

nw:page-rank

nw:page-rank

The page rank of a node can be thought of as the proportion of time that an agent walking forever at random on the network would spend at this node. The agent has an equal chance of taking any of a nodes edges, and will jump around the network completely randomly 15% of the time. In practice, like with eigenvector centrality, turtles that are connected to a lot of other turtles that are themselves well-connected (and so on) get a higher page rank.

Page rank is one of the several algorithms that search engines use to determine the importance of a website.

The sum of all page rank values should be approximately one. Unlike eigenvector centrality, page rank is defined for all networks, no matter the connectivity. Currently, it treats all links as undirected links.

As of now, link weights are not taken into account.

nw:closeness-centrality

nw:closeness-centrality

The closeness centrality of a turtle is defined as the inverse of the average of it’s distances to all other turtles. (Some people use the sum of distances instead of the average, but the extension uses the average.)

Note that this primitive reports the intra-component closeness of a turtle, that is, it takes into account only the distances to the turtles that are part of the same component as the current turtle, since distance to turtles in other components is undefined. The closeness centrality of an isolated turtle is defined to be zero.

nw:weighted-closeness-centrality

nw:weighted-closeness-centrality link-weight-variable

This is identical to nw:closeness-centrality, except that weights provided by the given variable are treated as the distances of links.

nw:clustering-coefficient

nw:clustering-coefficient

Reports the local clustering coefficient of the turtle. The clustering coefficient of a node measures how connected its neighbors are. It is defined as the number of links between the node’s neighbors divided by the total number of possible links between its neighbors.

nw:clustering-coefficient takes the directedness of links into account. A directed link counts as a single link whereas an undirected link counts as two links (one going one-way, one going the other).

The global clustering coefficient measures how much nodes tend to cluster together in the network in general. It is defined based on the types of triplets in the network. A triplet consists of a central node and two of its neighbors. If its neighbors are also connected, it’s a closed triplet. If its neighbors are not connected, it’s an open triplet. The global clustering coefficient is simply the number of closed triplets in a network divided by the total number of triplets. It can be calculated from the local clustering coefficient quite easily with the following code

Note that the above will only work with the default context, and may need to tweaked if you’ve set the turtles or links in the network to something other than turtles and links.

The average local clustering coefficient is another popular method for measuring the amount of clustering in the network as a whole. It may be calculated with

nw:modularity

nw:modularity

Modularity is a measurement of community structure in the network. It is defined based on the number of in-community links versus the number of between-community links. This primitive takes as input a list of agentsets, where each of the agentsets is one the communities that you’re separating the network into.

This measurement works on undirected, directed, and mixed-directedness networks. In the case of mixed-directedness, undirected links are treated essentially the same as two opposing directed links. It does not take weight into account.

Example:

nw:bicomponent-clusters

nw:bicomponent-clusters

Reports the list of bicomponent clusters in the current network context. A bicomponent (also known as a maximal biconnected subgraph) is a part of a network that cannot be disconnected by removing only one node (i.e. you need to remove at least two to disconnect it). The result is reported as a list of agentsets, in random order. Note that one turtle can be a member of more than one bicomponent at once.

nw:weak-component-clusters

nw:weak-component-clusters

Reports the list of “weakly” connected components in the current network context. A weakly connected component is simply a group of nodes where there is a path from each node to every other node. A “strongly” connected component would be one where there is a directed path from each node to every other. The extension does not support the identification of strongly connected components at the moment.

The result is reported as a list of agentsets, in random order. Note that one turtle cannot be a member of more than one weakly connected component at once.

nw:louvain-communities

nw:louvain-communities

Detects community structure present in the network. It does this by maximizing modularity using the Louvain method. The communities are reported as a list of turtle-sets.

Often you’ll want to tell turtles about the community that they are in. You can do this like so:

You can give each community its own color with something like this:

nw:maximal-cliques

nw:maximal-cliques

A clique is a subset of a network in which every node has a direct link to every other node. A maximal clique is a clique that is not, itself, contained in a bigger clique.

The result is reported as a list of agentsets, in random order. Note that one turtle can be a member of more than one maximal clique at once.

The primitive uses the Bron–Kerbosch algorithm and only works with undirected links.

nw:biggest-maximal-cliques

nw:biggest-maximal-cliques

The biggest maximal cliques are, as the name implies, the biggest cliques in the current context. Often, more than one clique are tied for the title of biggest clique, so the result is reported as a list of agentsets, in random order. If you want only one clique, use one-of nw:biggest-maximal-cliques.

The primitive uses the Bron–Kerbosch algorithm and only works with undirected links.

nw:generate-preferential-attachment

nw:generate-preferential-attachment turtle-breed link-breed num-nodes optional-command-block

Generates a new network using the Barabási–Albert algorithm. This network will have the property of being “scale free”: the distribution of degrees (i.e. the number of links for each turtle) should follow a power law.

In this version of the primitive, turtles are added, one by one, each forming one link to a previously added turtle, until num-nodes is reached. The more links a turtle already has, the greater the probability that new turtles form links with it when they are added. Future versions of the primitive might provide more flexibility in the way the network is generated.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

nw:generate-random

nw:generate-random turtle-breed link-breed num-nodes connection-probability optional-command-block

Generates a new random network of num-nodes turtles in which each one has a connection-probability (between 0 and 1) of being connected to each other turtles. The algorithm uses the G(n, p) variant of the Erdős–Rényi model.

The algorithm is O(n²) for directed networks and O(n²/2) for undirected networks, so generating more than a couple thousand nodes will likely take a very long time.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

nw:generate-watts-strogatz

nw:generate-watts-strogatz turtle-breed link-breed num-nodes neighborhood-size rewire-probability optional-command-block

Generates a new Watts-Strogatz small-world network.

The algorithm begins by creating a ring of nodes, where each node is connected to neighborhood-size nodes on either side. Then, each link is rewired with probability rewire-prob.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. Furthermore, the turtles are generated in the order they appear as in create-ordered-turtles. So, in order to lay the ring out as a ring, you can do something like:

nw:generate-small-world

nw:generate-small-world turtle-breed link-breed row-count column-count clustering-exponent is-toroidal optional-command-block

Generates a new small-world network using the Kleinberg Model. Note that nw:generate-watts-strogatz generates a more traditional small-world network.

The algorithm proceeds by generating a lattice of the given number of rows and columns (the lattice will wrap around itself if is-toroidal is true). The “small world effect” is created by adding additional links between the nodes in the lattice. The higher the clustering-exponent, the more the algorithm will favor already close-by nodes when adding new links. A clustering exponent of 2.0 is typically used.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

The turtles are generated in the order that they appear in the lattice. So, for instance, to generate a kleinberg lattice accross the entire world, and lay it out accordingly, try the following:

nw:generate-lattice-2d

nw:generate-lattice-2d turtle-breed link-breed row-count column-count is-toroidal optional-command-block

Generates a new 2D lattice network (basically, a grid) of row-count rows and column-count columns. The grid will wrap around itself if is-toroidal is true.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

The turtles are generated in the order that they appear in the lattice. So, for instance, to generate a lattice accross the entire world, and lay it out accordingly, try the following:

nw:generate-ring

nw:generate-ring turtle-breed link-breed num-nodes optional-command-block

Generates a ring network of num-nodes turtles, in which each turtle is connected to exactly two other turtles.

The number of nodes must be at least three.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

nw:generate-star

nw:generate-star turtle-breed link-breed num-nodes optional-command-block

Generates a star network in which there is one central turtle and every other turtle is connected only to this central node. The number of turtles can be as low as one, but it won’t look much like a star.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

nw:generate-wheel

nw:generate-wheel turtle-breed link-breed num-nodes optional-command-block

Variants:

Generates a wheel network, which is basically a ring network with an additional “central” turtle that is connected to every other turtle.

The number of nodes must be at least four.

The nw:generate-wheel only works with undirected link breeds. The nw:generate-wheel-inward and nw:generate-wheel-outward versions only work with directed link-breed. The inward and outward part of the primitive names refer to the direction that the “spokes” of the wheel point to relative to the central turtle.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

nw:save-matrix

nw:save-matrix file-name

Saves the current network, as defined by nw:set-context, to file-name, as a text file, in the form of a simple connection matrix.

Here is, for example, a undirected ring network with four nodes:

0.00 1.00 0.00 1.00
1.00 0.00 1.00 0.00
0.00 1.00 0.00 1.00
1.00 0.00 1.00 0.00

And here is the directed version:

0.00 1.00 0.00 0.00
0.00 0.00 1.00 0.00
0.00 0.00 0.00 1.00
1.00 0.00 0.00 0.00

At the moment, nw:save-matrix does not support link weights. Every link is represented as a “1.00” in the connection matrix. This will change in a future version of the extension.

nw:load-matrix

nw:load-matrix file-name optional-command-block

Generates a new network according to the connection matrix saved in file-name, using turtle-breed and link-breed to create the new turtles and links.

At the moment, nw:load-matrix does not support link weights.

Please be aware that the breeds that use use to load the matrix may be different from those that you used when you saved it.

For example:

will give you back undirected links, even if you saved directed links into the matrix.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

nw:save-graphml

nw:save-graphml file-name

You can save the current graph to GraphML. The following NetLogo code:

Will produce the following GraphML file:

<?xml version="1.0" encoding="UTF-8"?>
<graphml xmlns="http://graphml.graphdrawing.org/xmlns/graphml"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns/graphml">
<key id="PEN-MODE" for="node" attr.name="PEN-MODE" attr.type="string"/>
<key id="YCOR" for="node" attr.name="YCOR" attr.type="double"/>
<key id="PEN-SIZE" for="node" attr.name="PEN-SIZE" attr.type="double"/>
<key id="LABEL" for="node" attr.name="LABEL" attr.type="string"/>
<key id="SHAPE" for="node" attr.name="SHAPE" attr.type="string"/>
<key id="BREED" for="node" attr.name="BREED" attr.type="string"/>
<key id="WHO" for="node" attr.name="WHO" attr.type="double"/>
<key id="HIDDEN?" for="node" attr.name="HIDDEN?" attr.type="boolean"/>
<key id="LABEL-COLOR" for="node" attr.name="LABEL-COLOR" attr.type="double"/>
<key id="HEADING" for="node" attr.name="HEADING" attr.type="double"/>
<key id="BANK-NAME" for="node" attr.name="BANK-NAME" attr.type="string"/>
<key id="HOMETOWN" for="node" attr.name="HOMETOWN" attr.type="string"/>
<key id="COLOR" for="node" attr.name="COLOR" attr.type="double"/>
<key id="XCOR" for="node" attr.name="XCOR" attr.type="double"/>
<key id="SIZE" for="node" attr.name="SIZE" attr.type="double"/>
<key id="END1" for="edge" attr.name="END1" attr.type="string"/>
<key id="TIE-MODE" for="edge" attr.name="TIE-MODE" attr.type="string"/>
<key id="END2" for="edge" attr.name="END2" attr.type="string"/>
<key id="LABEL-COLOR" for="edge" attr.name="LABEL-COLOR" attr.type="double"/>
<key id="THICKNESS" for="edge" attr.name="THICKNESS" attr.type="double"/>
<key id="LABEL" for="edge" attr.name="LABEL" attr.type="string"/>
<key id="SHAPE" for="edge" attr.name="SHAPE" attr.type="string"/>
<key id="BREED" for="edge" attr.name="BREED" attr.type="string"/>
<key id="COLOR" for="edge" attr.name="COLOR" attr.type="double"/>
<key id="AMOUNT" for="edge" attr.name="AMOUNT" attr.type="double"/>
<key id="HIDDEN?" for="edge" attr.name="HIDDEN?" attr.type="boolean"/>
<graph edgedefault="undirected">
<node id="client 1">
<data key="PEN-MODE">up</data>
<data key="YCOR">0</data>
<data key="PEN-SIZE">1</data>
<data key="LABEL"></data>
<data key="SHAPE">default</data>
<data key="BREED">clients</data>
<data key="WHO">1</data>
<data key="HIDDEN?">false</data>
<data key="LABEL-COLOR">9.9</data>
<data key="HEADING">356</data>
<data key="HOMETOWN">Turtle City</data>
<data key="COLOR">115</data>
<data key="XCOR">0</data>
<data key="SIZE">1</data>
</node>
<node id="banker 0">
<data key="PEN-MODE">up</data>
<data key="YCOR">0</data>
<data key="PEN-SIZE">1</data>
<data key="LABEL"></data>
<data key="SHAPE">default</data>
<data key="BREED">bankers</data>
<data key="WHO">0</data>
<data key="HIDDEN?">false</data>
<data key="LABEL-COLOR">9.9</data>
<data key="HEADING">32</data>
<data key="BANK-NAME">The Bank</data>
<data key="COLOR">85</data>
<data key="XCOR">0</data>
<data key="SIZE">1</data>
</node>
<edge source="client 1" target="banker 0">
<data key="END1">(client 1)</data>
<data key="TIE-MODE">none</data>
<data key="END2">(banker 0)</data>
<data key="LABEL-COLOR">9.9</data>
<data key="THICKNESS">0</data>
<data key="LABEL"></data>
<data key="SHAPE">default</data>
<data key="BREED">accounts</data>
<data key="COLOR">5</data>
<data key="AMOUNT">9999.99</data>
<data key="HIDDEN?">false</data>
</edge>
<edge source="banker 0" target="client 1">
<data key="END1">(banker 0)</data>
<data key="TIE-MODE">none</data>
<data key="END2">(client 1)</data>
<data key="LABEL-COLOR">9.9</data>
<data key="THICKNESS">0</data>
<data key="LABEL"></data>
<data key="SHAPE">default</data>
<data key="BREED">friendships</data>
<data key="COLOR">5</data>
<data key="HIDDEN?">false</data>
</edge>
</graph>
</graphml>

A few things to notice:

nw:load-graphml

nw:load-graphml file-name optional-command-block

Loading a GraphML file into NetLogo with the network extension should be as simple as calling nw:load-graphml "example.graphml", but there is a bit of preparation involved.

The key idea is that nw:load-graphml will try to assign the attribute values defined in the GraphML file to NetLogo agent variables of the same names (this is not case sensitive). The first one it tries to set is breed if it is there, so the turtle or link will get the right breed and, hence, the right breed variables.

One special case is the who number, which is ignored by the importer if it is present as a GraphML attribute: NetLogo does not allow you to modify this number once a turtle is created and, besides, there could already be an existing turtle with that number.

The simplest case to handle is when the original GraphML file has been saved from NetLogo by using nw:save-graphml. In this case, all you should have to do is to make sure that you have the same breed and variables definition as when you saved the file and you should get back your original graph. For example, if you want to load the file from the nw:save-graphml example above, you should have the following definitions:

Loading a graph that was saved from a different program than NetLogo is quite possible as well, but it may take a bit of tinkering to get all the attribute-variable match up right. If you encounter major problems, please do not hesitate to open an issue.

The extension will try to assign the type defined by attr.type to each variable that it loads. If it’s unable to convert it to that type, it will load it as a string. If attr.type is not defined, or is set to an unknown value, the extension will first try to load the value as a double, then try it as a boolean, and finally fall back on a string.

If you specify an optional-command-block, it is executed for each turtle in the newly created network. For example:

Note that this command block can be used to build a list or an agentset containing the newly created nodes:

nw:load

nw:load file-name default-turtle-breed default-link-breed optional-command-block

Filetype specific variants:

Import the given file into NetLogo. Like nw:load-graphml, the importer will do its best to match node and edge attributes in the file with turtle and link variables in NetLogo. If breed is specified for nodes and edges in the file and exists in NetLogo, it will be used. Otherwise, the default turtle and link breeds are used.

Limitations:

nw:load determines the file-type of given file based on the extension and calls the corresponding load-* primitive on it. Note that GraphML must be imported with nw:load-graphml.

nw:save

nw:save file-name

Filetype specific variants:

Export the network context in the given format to the given file. Turtle and link attributes will be exported to formats that support node and edge properties.

Limitations:

nw:save determines the file-type of the given file based on the extension and calls the corresponding save-* primitive on it. Note that GraphML must be exported with nw:save-graphml.