Semi-supervised clustering algorithm for community structure detection in complex networks

Semisupervised Community Detection by Voltage Drops

“…In this subsection, four classical complex networks with known community structures are selected to test the introduced algorithm. The description of these four networks can be found everywhere [1,11,16,23]. Taking Zachary Karate Club network with two communities, for example, we first choose randomly one node in each community and label it.…”

“…We now present our experimental results on the LFR benchmark and further compare our proposal with GN algorithm [24], spectral clustering algorithm [1], NMF algorithm [20], and SNMF-SS algorithm [11] by a normalized mutual information index (NMI).…”

“…Each graph has 1000 vertices, average degree of 15, maximum degree of 50, maximum for the community sizes of 50, and minimum for the community sizes of 5. The definition of NMI can be found everywhere [11,15,26].…”

“…In fact, some prior information is of great value in identifying the community structure. Based on the discussion of an equivalence of the objective functions of the symmetric nonnegative matrix factorization and the maximum optimization of modularity density, Ma et al [11] introduced a semisupervised clustering algorithm for 2 Mathematical Problems in Engineering community structure detection. In [12], Silva and Zhao presented a technique for semisupervised classification tasks, by using the modularity measure of complex networks, originally designed for unsupervised learning tasks.…”

Overlapping community detection in complex networks using symmetric binary matrix factorization

“…Thanks to the flexibility of the model, NMF is particularly suitable for the detection of overlapping communities [8,[23][24][25]. The result U can be interpreted as a cluster membership degree matrix, i.e., node i belongs to a community t with the strength U it (note that j U ij = 1), providing "fuzzy" overlapping communites.…”