# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### AE-Based Community Detection Strategies  
Autoencoders (AEs) are very common in community detection because of their ability to to represent efficiently nonlinear real-world networks. The deep neural network based auto-encoders are used to learn data codings (representations of data sets) in an unsupervised manner. They are a tool used for dimensionality reduction. They manage to implement a proper pre-processing of the adjacency matrix, in order to improve the spatial proximity [21; 22; 23; 24; 25]. The embeddings provided have nonlinear properties and thus the auto-encoder is suitable for mapping data points into the low-dimensional space, as required in community detection. In [26], a new approach to combine the models of modularity and normalized-cut via an auto-encoder was proposed. The modularity approach aims at maximizing the _modularity measure_, that is, the number of edges in the original graph divided by the expected number of edges in a random graph that has no specific community structure. In the normalized-cut approach, the aim is to cut the minimum number of edges and have the vertices of the same partition possess a high similarity and the vertices between different partitions possess low similarity. A unified objective function is used to reconstruct the combination matrix composed of the modularity and the Markov matrix. The auto-encoder is used to produce a mapping to the low-dimensional space, which can reconstruct the joint matrix. A multi-layer auto-encoder is introduced by cascading a number of auto-encoders. In this regard, the network is trained on a \"per layer\" basis. The multi-layer autoencoder provides powerful encoding with both topological and content information, which is very efficient for network community detection. Experiments were conducted using the NMI as a comparison metric. The experimental results have shown that this combination indeed improves community detection, compared to approaches that use topological or content information alone. The computational time complexity of the proposed scheme is \(O(N)\), which is linear to the number of graph nodes. In [27], DeCom, a model is proposed that leverages the idea of autoencoder pipelines used to extract overlapping communities in large-scale networks. The DeCom strategy is composed of three phases: The first phase is the seed selection phase, where the features of the network's vertices are learned in a stepwise greedy approach. The auto-encoder tries to learn the values associated with the vertices by mapping the data degree matrix to a matrix with fewer dimensions. In the second phase, the topology is learned; the goal here is to get multiple communities in a completely unsupervised manner, where each community is defined by the seed. The communities are expanded from each seed using a random walk with the seed as the starting point. Thus, each community includes the seed and nodes closed to the seed (thus, highly influential). By multiple iterations, the influence keeps spreading linearly, with label information, to the neighboring nodes. In the third phase, the formed clusters are refined using modularity optimization, that is, by finding better representations by reconstruction of the network structure. Compared to similar works [28; 29; 30; 31], the DeCom strategy updates the values of vertices in multiple iterations to minimize the reconstruction error. This results in better graph coverage (% of vertices assigned to clusters). In addition, compared to well known LPA (label propagation algorithms) like COPRA, this reconstruction minimization error recovers the vertices which are lost in LPAs due to not obtaining a label. The cost of initializing \(n\) vertices in an \(l\)-layered auto-encodes is \(O(l(n))\). The seed selection time is linear to the number of edges \(z\), \(O(z)\), and it takes \(O(n\log(n))\) to sort the degrees in ascending order of their influence.