| multinet.communities {multinet} | R Documentation |
Community detection algorithms and evaluation functions
Description
Various algorithms to compute communities in multiplex networks, based on flattening (flat_ec, weighted, and flat_wc, unweighted), frequent itemset mining (abacus), adjacent cliques (clique percolation), modularity optimization (generalized louvain), random walks (infomap) and label propagation (mdlp). glouvain2_ml is a more efficient implementation of the original glouvain_ml, no longer based on matrices: it is equivalent to glouvain_ml with gamma set by default to 1.0 (apart from undeterministic behaviour: individual executions are not guaranteed to return the same result). get_community_list_ml is a commodity function translating the result of these algorithms into a list of vertex identifiers, and is internally used by the plotting function.
There are also algorithms to evaluate the resulting communities: generalized modularity (as optimized by glouvain) and normalized mutual information (nmi_ml) and omega index (omega_index_ml) to compare respectively partitioning and general communities. Please consider that both comparison functions use the number of vertices in the network to make a computation, so the absence of actors from some layers would change their result.
Usage
abacus_ml(n, min.actors=3, min.layers=1)
flat_ec_ml(n)
flat_nw_ml(n)
clique_percolation_ml(n, k=3, m=1)
glouvain_ml(n, gamma=1, omega=1)
infomap_ml(n, overlapping=FALSE, directed=FALSE, self.links=TRUE)
mdlp_ml(n)
modularity_ml(n, comm.struct, gamma=1, omega=1)
nmi_ml(n, com1, com2)
omega_index_ml(n, com1, com2)
get_community_list_ml(comm.struct, n)
Arguments
n |
A multilayer network. |
min.actors |
Minimum number of actors to form a community. |
min.layers |
Minimum number of times two actors must be in the same single-layer community to be considered in the same multi-layer community. |
k |
Minimum number of actors in a clique. Must be at least 3. |
m |
Minimum number of common layers in a clique. Not to be confused with number of edges, as it is meant in the summary function (here we use the notation of the paper introducing this algorithm). |
gamma |
Resolution parameter for modularity in the generalized louvain method. |
omega |
Inter-layer weight parameter in the generalized louvain method. |
overlapping |
Specifies if overlapping clusters can be returned. |
directed |
Specifies whether the edges should be considered as directed. |
self.links |
Specifies whether self links should be considered or not. |
comm.struct |
The result of a community detection method. |
com1 |
The result of a community detection method. |
com2 |
The result of a community detection method. |
Value
All community detection algorithms return a data frame where each row contains actor name, layer name and community identifier.
abacus_ml, flat_ec_ml, flat_nw_ml, clique_percolation_ml, and glouvain_ml are only implemented to work with undirected networks. clique_percolation_ml automatically considers the network to be undirected even if the edges are directed. glouvain_ml also considers weights, if *all* layers have a DOUBLE attribute named w_.
The evaluation functions return a number between -1 and 1. For the comparison functions, 1 indicates that the two community structures are equivalent. The maximum possible value of modularity is <= 1 and depends on the network, so modularity results should not be compared across different networks. Also, notice that modularity is only defined for partitioning community structures.
get_community_list_ml transforms the output of a community detection function into a list by grouping all the nodes having the same community identifier and the same layer. Notice that:
The numbers in the result of get_community_list_ml() correspond to vertices. Number X refers the the Xth vertex as returned by vertices_ml(ml).
This function splits the communities by layer. That is, every community corresponds to multiple entry in the generated list (in general), all with the same value of $cid.
References
Berlingerio, Michele, Pinelli, Fabio, and Calabrese, Francesco (2013). ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS. Data Mining and Knowledge Discovery, 27(3), 294-320. (for abacus_ml())
Afsarmanesh, Nazanin, and Magnani, Matteo (2018). Partial and overlapping community detection in multiplex social networks. Social informatics (for clique_percolation_ml())
Mucha, Peter J., Richardson, Thomas, Macon, Kevin, Porter, Mason A., and Onnela, Jukka-Pekka (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science (New York, N.Y.), 328(5980), 876-8. Data Analysis, Statistics and Probability; Physics and Society. (for glouvain_ml())
Michele Berlingerio, Michele Coscia, and Fosca Giannotti. Finding and characterizing communities in multidimensional networks. In International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 490-494. IEEE Computer Society Washington, DC, USA, 2011 (for flat_ec_ml() and flat_nw_ml())
De Domenico, M., Lancichinetti, A., Arenas, A., and Rosvall, M. (2015) Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems. PHYSICAL REVIEW X 5, 011027 (for infomap_ml())
Oualid Boutemine and Mohamed Bouguessa. Mining Community Structures in Multidimensional Networks. ACM Transactions on Knowledge Discovery from Data, 11(4):1-36, 2017 (for mdlp_ml())
See Also
Examples
net <- ml_florentine()
abacus_ml(net)
flat_ec_ml(net)
flat_nw_ml(net)
clique_percolation_ml(net)
glouvain_ml(net)
infomap_ml(net)
mdlp_ml(net)
# evaluation
c1 <- glouvain_ml(net)
modularity_ml(net, c1)
c2 <- flat_ec_ml(net)
nmi_ml(net, c1, c2)
c3 <- abacus_ml(net)
omega_index_ml(net, c1, c2)