# A Survey on the Recent Advances of Deep Community Detection
## 4 Future Trends  
The strategies we have presented are the most recent developments in community detection strategies over the last two years. From our study, we have spotted several issues that need to be further elaborated, and improved solutions must be provided. In this section, we provide some of these issues that may lead the way for future trends. 1. **Large scale of modern networks:** The analysis of modern social networks becomes rather cumbersome, as their size and number keeps growing larger and larger. Older methods tried to employ some level of parallelism, but these suffered from low speedups. Generally, there is still a growing need for algorithms that do not fail to scale with the increasing number of users and the growing complexity of their relationships. Moreover, the existing schemes that were presented in this work generally have not been tested on graphs with billions of vertices or nodes using more powerful machines. 2. **Dynamic Community Detection:** Dynamic community detection requires that the changes in user relationships and in the overall network topology should also be considered. This means that models that extract spatio-temporal features of the social networks have to be developed. Generally, the irregular topologies, the frequent changes of relationships among users, and the network updates pose certain burdens on the processing of synchronous social networks, and more computational efforts are now required to accommodate these changes. 3. **Better use of computational resources:** Certain computations performed necessarily require the consumption of large quantities of computational resources. For example, in [45], the authors admit that the similarity matrix is obtained through processing of the adjacency matrix, and the calculations involved require large memory consumption and powerful experimental equipment. Thus, strategies that consume large amounts of resources or even exhaust the existing ones should be equipped with some type of decomposition strategy, which also may be accompanied with a well-designed parallelism scheme. 4. **Community overlapping:** As mentioned during the description of the most recent schemes in Section 2, most of the strategies presented do consider the situation of community overlaps (some exceptions have been mentioned in the text). In the future, most of these works should be expanded so that this very important issue is taken into account. 5. **Meaningful semantic representations of communities:** The community detection problem is a well-studied problem, and the first papers were published back in the beginning of this century. However, the datasets being used as inputs to construct communities contain rather discrete information like well-used words, symbols, forms of categorization for professions or other attributes or short phrases. Better datasets should be developed and used in deep learning strategies to extract and even predict communities. Generally, the vast amounts of information produced by the social networks should be used in a better and more informative way, in order to produce a more meaningful semantic representation of communities. In addition, the better interpretation of specific communities (medical, sport, politic, etc.) is closely related to this direction. 6. **Natural Language Processing (NPL embeddings):** A recent trend is to compute node embeddings by using the second order random walks [72]. These embeddings aim at keeping similar nodes close in their representations. Simplicial complexes represent a promising way for representing embeddings, but still the community extraction remains a challenge to be dealt with [73]. Other future trends include the ego networks, the streaming community detection (graph streams), and the temporal graphs. 7. **More straightforward comparisons are needed:** During our study, we have noticed that there is a lack of straightforward comparisons among the presented strategies. Perhaps, tools like the NetworKit [74] can be helpful in this regard.