# Deep Learning-Based Community Detection Approach on Multimedia Social Networks
## 5 Conclusions  
Currently, we are inundated with a large amount of data from which it becomes increasingly difficult to infer new information. One of the methods to improve knowledge about a particular field of interest is the analysis of social networks through the analysis of human relationships that are established between people. Given the large number of friendship relationships established on modern social networks today, identifying subsets of users who share common interests is becoming increasingly difficult. The identification of such groups of users can be useful in different fields: marketing, with the suggestion of appropriate advertising campaigns, and recommendation systems, with the suggestion of appropriate tourist/cultural routes based on their preferences. In this paper, a methodology was proposed for modeling heterogeneous information in a hypergraph-based data model and for identifying user groups by combining their preferences and the common actions performed by users on an MSN. The choice to determine communities based on actions allowed reducing the number of relationships established between users, allowing a more granular analysis of the interests and interactions between users. In particular, the analysis of the actions performed by each user on multimedia objects allowed analyzing this behavior within the social network, providing more details about these preferences and interests compared to those of other people; in fact, the addition of paths between cultural objects also allowed the identification of new ways in which two users can interact and thus share similar interests. Although our approach is promising, we need to discuss its limitations: to use the CNN, one needs to have a GPU-based infrastructure available; moreover, performing parameter tuning, i.e., choosing the learning rate, kernel, and number of epochs, is an expensive process in terms of computational complexity due to the massive matrix input. Future works will be devoted to:  
* Extending the testing phase to other datasets such as Facebook, Twitter, etc., not only with respect to Flickr;
* Optimizing the analysis of influence and recommendation algorithms;
* Reducing the impact of _lurkers_ (a type of behavior on social media in which a user interrupts an online silence or passive thread-viewing habit to engage in a virtual \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **\% Labeled Nodes** & **1\%** & **3\%** & **6\%** & **9\%** \\ \hline \multirow{4}{*}{Micro-F1 (\%)} & CNN & 25.94 & 35.88 & 39.43 & 44.51 \\ \cline{2-6}  & DeepWalk & 32.40 & 35.90 & 37.70 & 38.50 \\ \cline{2-6}  & SDNE & 23.74 & 34.76 & 37.83 & 41.14 \\ \cline{2-6}  & LINE & 23.01 & 34.44 & 37.75 & 40.65 \\ \cline{2-6}  & GraphGAN & 23.01 & 33.10 & 37.77 & 42.32 \\ \cline{2-6}  & CANE & 23.47 & 33.49 & 37.89 & 42.54 \\ \cline{2-6}  & DNNNC & 22.79 & 32.99 & 37.14 & 42.16 \\ \hline \multirow{4}{*}{Macro-F1 (\%)} & CNN & 12.15 & 20.91 & 26.46 & 29.74 \\ \cline{2-6}  & DeepWalk & 14.00 & 19.60 & 22.90 & 24.60 \\ \cline{2-6}  & SDNE & 11.69 & 19.87 & 23.29 & 26.13 \\ \cline{2-6}  & LINE & 11.52 & 19.76 & 23.01 & 25.78 \\ \cline{2-6}  & GraphGAN & 13.92 & 19.91 & 25.63 & 27.39 \\ \cline{2-6}  & CANE & 14.08 & 19.97 & 25.73 & 27.56 \\ \cline{2-6}  & DNNNC & 13.78 & 19.66 & 25.21 & 27.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of our approach w.r.t. DeepWalk, SDNE, LINE, GraphGAN, CANE, and DNNNC on the Flickr dataset. conversation; the term implies that a user typically does not participate in social media or online social activities) based on communities in social networks;
* Improving the community detection also using _embedding_ techniques directly on hypergraphs, which transform a state space with high dimensionality into a new space with low-dimensionality (e.g., vectors), having the advantages of being able to lighten the computational complexity and to apply many techniques of machine learning. A.F. : Conceptualization, Methodology Software, Validation, Writing--Original Draft, Writing--Review & Editing; V.M.