Integrate communities in a Java project

By importing the SNA package, it is possible to use the community partitions of the network in a Java project, as well as detect these partitions, or define novel community partition approaches. In this section we clarify how this can be done.

Communities

The communities in a network represent a partition of the users. A node can only belong to one of these groups. In order to manipulate community partitions, we include different interfaces in the framework.

The most important is the Communities class, which stores one of these community partitions. It identifies the different communities with integer numbers, and has the following methods:

int getNumCommunities()

This method obtains the number of clusters in the partition.

IntStream getCommunities()

This method obtains an stream with the identifiers of the considered community partitions.

int getCommunity(U user)

Given a user in the network, it obtains the identifier of the community it belongs to.

Arguments:

  • user: the identifier of the user.

Returns

  • the identifier of the community it belongs to, or -1 if the user does not belong to any community.

Stream<U> getUsers(int community)
Arguments:

  • community: the community identifier.

Returns:

  • an stream containing the users in the community.

void addCommunity()

This method adds a community to the partition.

boolean add(U user, int comm)

This function a user to a community.

Arguments:

  • user: the user.

  • comm: the community identifier.

Returns:

  • true if the user is added, false otherwise.

int getCommunitySize(int comm)

Finally, this method counts the number of users in a given community.

Arguments:

  • comm: the community identifier.

Returns:

  • the number of users in the community.

Dendograms

Sometimes, community detection algorithms do not only find a community partition, but what we call a dendogram: a tree containing different community partitions, where the leaves represent the nodes in the network, and intermediate nodes consider the union of the communities represented by their children.

We provide the class Dendogram for reading and working with these structures, allowing to get community partitions by the number of communities, or by obtaining communities of a given size.

It has the following methods:

Tree<U> getTree()

This method obtains the underlying tree of the dendogram.

Returns:

  • the underlying tree of the dendogram.

Communities<U> getCommunitiesByNumber(int n)

Obtains a community partition containing, at most, n communities.

Arguments:

  • n: the maximum number of communities.

Returns

  • the desired community partition, or null if something failed.

Map<Integer, Communities<U>> getCommunitiesByNumber()

It obtains all the possible community partitions by number.

Returns

  • a map, indexed by the number of communities, containing the different community partitions.

Communities<U> getCommunitiesBySize(int size)

It obtains a partition of the network where the maximum number of users on each community is provided.

Arguments:

  • size: the maximum number of users on each community.

Returns

  • the community partition if everything goes well, null otherwise.

Map<Integer, Communities<U>> getCommunitiesBySize()

It obtains all the possible community partitions by size.

Returns

  • a map, indexed by the maximum community size, containing the different community partitions.

Community detection algorithms

In order to detect communities in a network, we can use many different algorithms. Community detection algorithms inherit the CommunityDetectionAlgorithm interface. This interface must be implemented in case we want to develop novel approaches. It has the following methods:

Communities<U> detectCommunities(Graph<U> graph)

This method, given a network, identifies the community partition according to this algorithm.

Arguments:

  • graph: the social network graph for detecting communities.

Returns

  • the community partition if everything goes well, null otherwise.

Then, if the algorithm can find a dendogram, it also inherits the DendogramCommunityDetectionAlgorithm interface, which adds the following method:

Dendogram<U> detectCommunityDendogram(Graph<U> graph)

which finds the dendogram of the network.

Arguments:

  • graph: the social network graph for detecting communities.

Returns

  • the dendogram if everything goes well, null otherwise.