was on discovering communities in monoplex or single-layer networks [6]. Recently, various approaches have evolved to discover communities in multiplex networks [7]. Earlier multiplex network community detection approaches interpreted the multiplex network as a monoplex network by aggregating the different layers into a single-layer, then discovering the community structure by applying traditional simple graph community detection techniques [8]. However, these techniques are not appropriate to reveal the true community structure, as they do not consider the proprieties carried in each layer. To cope with the disadvantages of the aggregation-based methods, other multiplex community detection techniques have been recently proposed. In [9], a unified non-negative matrix factorization algorithm that aimed to reveal the set of within- and across-layer clusters in fully-connected multi-layer networks was introduced, where the nodes were connected within and across the layers. The authors in [10] presented an approach to investigate a multi-view network structure based on a spectral clustering technique. However, they did not consider the effect of the individual layers on the common subspace. In [11], the authors presented a one parameter family of matrix power means (PM) to investigate the communities in multi-layer networks. The PM method merges the Laplacians from the separate layers and then analyzes them in the stochastic block model. In [12], two methods that aim to investigate the Laplacian rank constrained graph, which is considered as the centroid of the constructed graph for each layer with different confidences, were proposed. In the first method, the layers' weights are learnt by introducing a hyper-parameter, whereas the second method is self-weighted. A top-bottom network partitioning strategy was proposed in [13], employing cross-layer edge clustering coefficients to assess the possibility of separating the communities. In [14], a robust multi-view spectral clustering (RMSC) technique was proposed. The proposed RMSC uncovered the clusters in the network based on decomposing the transition probability matrix into low rank and sparse components. In [15], the clusters in multi-layer networks were detected based on the Grassmann manifold theory through a modified spectral clustering approach (SCML). The proposed SCLM revealed the community structure of each layer by applying normalized spectral clustering for each layer individually. On the other hand, a centroid-based co-regularization (CentroidCoreg) for spectral clustering was proposed in [16], detecting the community structure based on a common subspace across the layers. In [17], the proposed method relied on the probability distribution of each layer to cluster the multiplex network. In [18], an approach that relied on graph factorization to explore the consensus, as well as the complementary information coming from the different views (2 CMV), was presented. In [19], the authors proposed an algorithm (DiMMA) that captured the diversity and complexity of the data from multiple perspectives in order to provide a more accurate and reliable clustering solution. This was achieved by incorporating the distance information from the different views. Nevertheless, the abovementioned techniques are less robust in real-world conditions, as they do not perform well in noisy networks. In [20], the authors proposed to detect the clusters in multiplex networks through joint non-negative matrix factorization (NMF), where multiple formulations of the objective function were introduced: (i) collective symmetric NMF (CSNMF), (ii) collective projective NMF (CPNMF) and (iii) collective symmetric non-negative matrix trilactorization (CSNMTF). In these approaches, a set of non-negative low-dimensional feature representations were found first for the individual layers. The individual non-negative embeddings were then fused into a common feature representation through collective factorization.