# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### CNN and GNN Based Approaches
#### 3.2.3 Comments on the GNN/CNN Based Community Detection Schemes  
After the study of the most recent CNN/GNN-based community detection methods, we made the following observations:  
1. **The CNN/GNN approaches consider both data structure and node features:** The recent deep clustering strategies combine feature extraction and dimensionality reduction in a model which enables deep neural networks to learn suitable representations based on the criteria of the clustering module being used. These strategies usually focus their attention on the data features and not on the structure of the data taken into account during representation learning. This structure reveals the similarity among samples, and therefore is rather useful on representation learning. The emerging CNN/GNN approaches encode the graph structure and node features for the representation. The structural information is very important in data representation learning. 2. **The CNN/GNN based community detection strategies are basically semi-supervised or supervised learning strategies:** Typically, the real networks are unique in the sense that the training data of one network cannot be properly used in a network with a different structure. When communities are located, the only data that can be used for analysis is the same network's data. Schemes that apply semi-supervised learning strategies can be employed so that a network's node label information can be used to predict community features for the remaining non-labeled nodes of the same network. These strategies are sometimes very expensive. In this regard, there are CNN/GNN strategies that exploit the strength of auto-encoders to develop automatic feature learning [24; 49]. 3. **Many real-world networks are characterized, at a large scale, by skewed class distributions:** This is because different network parts may evolve without restrictions. For example, different nodes may be topologically connected with multiple other nodes, so the node class assignment largely depends on its connected nodes. Representation learning through accurately identified boundaries for each class cannot be easily achieved because each class is highly affected by the neighboring classes in the graph. The problem of imbalanced class distributions is hardly addressed in most of the papers, which assume or purposefully provide balanced label distributions for each class. A solution is given in [22], where a two-layered graph network is used to extract node representations trained on imbalanced class labels. 4. **Performance degrades as graph convolutional layers are added:** As the depth of the convolutional network is increased, performance declines, so does the quality of the neighbor propagation. This is due to the fact that the addition of many convolutional layers is similar to multiple Laplacian smoothing procedures. This in fact smooths the node features and the nodes from different clusters become indistinguishable (in the sense that their attribute values converge and they become equal). The proposed strategies try to keep the number of layers to relatively small numbers (up to three, some examples are [48; 49; 57; 60]). The ladder-shaped architecture proposed by [65] et al. is another idea to overcome this issue. 5. **Real-world graphs are formed by a combination of too many latent factors (for example, the same likes, hobbies, professions, etc.)**. Removing these factors from a graph is a difficult procedure: to learn the node representations free from such factors may need extraction of subsets of a neighborhood, in a quite complex network. Moreover, there are factors that may connect two people which are somehow overlapping (a football fan and a fan of a specific football club). Without completely distinguishing them (or making them independent), redundant representations will appear. Therefore, special attention should be given to implement techniques that support representation learning that discovers independent latent factors in a graph network. Ref. [66] is such an example. 6.