# Uncovering Hidden Community Structure in Multi-Layer Networks
## 2 Related Work  
In past years, a tremendous amount of community detection algorithms have been introduced in different categories (overlapping [8,9], disjoint [5,10], clustering [11], etc.) to understand the structure and fundamentals of different kinds of networks. To the best of our knowledge, the examination to detect the hidden communities in the network has nearly begun to gain the attention of various researchers.  
In [12], authors have studied fraud detection in networks and modeled an online auction network with fraudsters as a random network with hidden communities (fraudsters and associated accomplices) and proposed a maximum likelihood framework to detect the fraudsters, which is an iterative message-passing algorithm to heuristically solve the maximum likelihood detection problem. Generally, it has been observed that there is a possibility of clustering data in multiple alternative ways, depending on the requirements. The example of this scenario has been explained in [13], where data points of one cluster can belong to different clusters in some other ways.  
[PERSON] et al. [14] studied the most recent issue going around the world, i.e., the risk from an epidemic outbreak, to reveal the hidden communities of the affected or those to be affected, and analyzed the contact patterns from a public transit assignment model in a major metropolitan city. This work proposed two networks; first, the transfer network, which can reveal passenger groups that travel together on a given day. Second s the community network, which is derived from the transfer network and captures the similarity of travel patterns among passengers. Furthermore, [PERSON] et al. [15] proposed the Hidden Community based on Neighborhood Similarity Computation (HCNC) method based on Neighborhood Similarity Computation, assuming the sub-communities as the hidden or hierarchical communities, which can detect hidden communities irrespective of density variation within the community. [PERSON] et al. [16] remove nodes or edges dependent on the local Fiedler Vector Centrality (LFVC) which is related to the affectability of the algebraic connectivity to node or edge evacuations. Above all, they characterize an idea of profound network as an associated part that must be seen after removing nodes or edges from the remainder of the network.  
There have been numerous attempts to address community detection problems in multilayer networks through diverse approaches, e.g., identifying communities in temporal networks by modularity-maximization [17] where it emphasizes the difference between \"null networks\" and \"null models\" in modularity maximization and discusses the effect of interlayer edges on the multilayer modularity-maximization problem. [PERSON] et al. (2018) proposed a generative model for multilayer networks, which can be used to aggregate layers into clusters or to compress a dataset by identifying especially relevant or redundant layers.  
The proposed method clearly defines the technique of its operation in how it breaks the network first and how it reforms it again. The above-mentioned methods used similarity measures to detect the most similar nodes and form communities, but it has been examined that similarity is not always the quick fix. The proposed work is also influenced by the LFVC regarding the removal of either nodes or edges but specifically focuses on edge removal to make it more simplified.