# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### CNN and GNN Based Approaches
#### 3.2.2 GNN-Based Community Detection  
GNNs technically combine graph mining and deep learning and thus they are capable of modeling and detecting the underlying relationships in graph-based data. An interesting approach of GNN based schemes employ attention networks to capture the importance of the neighboring nodes to a target node. A representative work is the DAEGC algorithm (Deep Attentional Embedded Graph Clustering) [56], which develops a graph attentional autoencoder to learn latent representation and rank the importance of attributed nodes within a neighbor. More specifically, DAEGC uses attributed graphs to explore the information found in graphs. The framework consists of two parts: (1) an attention network auto-encoder that captures the affect of the neighboring nodes to a specific (target) node and, in this way, it compactly encodes the topology structure of a network, and (2) a self training clustering module that performs clustering based on the learned representation, and, in return, manipulates the latent representation according to the current clustering result. A deep architecture is built by stacked layers of encoders. [PERSON] and [PERSON] [57] addressed the issue of overlapping communities, that is, communities with members in common. They introduce a GNN based model for overlapping community detection, which are used as benchmarks. They combine the GNNs with the Bernoulli-Poisson model that is able to produce a variety of community topologies (e.g., nested, hierarchical), and lead to dense overlaps between communities. The computational time complexity is \(O(N+M)\), where \(N\) is the number of nodes and \(M\) is the number of edges of the network. In addition, the authors pre-processed four real-world datasets, to satisfy the criterion of possessing reliable information on node attributes and ground-truth overlapping communities: Chemistry, Computer Science, Medicine, and Engineering (networks, constructed from the Microsoft Academic Graph). A two level GNN model was used, with batch normalization after the first graph convolution layer. In addition, in this model, the weight matrices undergo a regularization process. [PERSON] et al. [58] introduced a spectral domain convolutional architecture for deep learning on graphs. The basis of the model is a class of parametric rational complex functions (Cayley polynomials), which efficiently compute spectral filters on graphs. The main advantage of these filters compared to other schemes that use such filters [59] is their ability to detect narrow frequency bands of importance during training. The model produces localized in space filters, and its time complexity is linear to the input data for sparsely connected graphs. The Cayley polynomials are real-valued functions with one real, some complex coefficients, and a spectral zoom parameter which can be tuned to zoom in to different parts of the spectrum. In this way, different frequency band filters can be obtained. The Cayley filters use basic matrix operations such as powers, additions, multiplications by scalars, and inversions, so the application of the filter does not require expensive eigendecomposition of the Laplacian operator. For a sparse graph with \(n\) edges, the application of a Cayley filter on a signal requires \(O((K+1)rn)\) operations, where \(K\) is the number of times that vertices exchange information with their neighbors, and \(r\) is the number of filter coefficients. In [60], a strategy that incorporates an AE to a GNN is proposed. A delivery operator is used to transfer the representations learned by auto-encoder to the corresponding layer. In addition, a dual self-supervised mechanism is used as guidance for the whole model updates. The proposed model is composed of a K-Nearest Neighbor (KNN) graph, a Deep Neural Network and a GNN network layer, and a dual self-supervised model. For a KNN network with \(N\) samples and a dimension of \(d\), the proposed model finds, per sample, the top \(K\) similar neighbors and set edges to connect it these neighbors.