# Deep Learning-Based Community Detection Approach on Multimedia Social Networks
## 2 Related Works
### Social Network Modeling  
The first proposal for modeling an OSN considers only users and their interactions. Exploiting this model, different approaches have been proposed with respect to particular applications, such as linker identification [5; 6], influence analysis [7; 8], and expert finding [9; 10]. However, these approaches do not consider the contribution made by multimedia content. To this end, more complex models have been proposed for social information networks, which can be classified into four categories, summarized in Table 1.  
In the first family, a social network is represented as a graph whose set of vertices is heterogeneous. Using such a model, [PERSON] et al. [11] proposed an algorithm that combines both the content and information context of the network for multimedia data annotation. In turn, [PERSON] et al. [12] used graph modeling and multimedia content information to propose a new concept of image similarity. The second family models the social network via a bipartite model. [PERSON] et al. [13] designed a bipartite graph to model the interaction between users and multimedia content to analyze the content diffusion in a social network. In [14], the authors proposed a social recommendation framework based on an embedding method for general bipartite (user-item) graphs. The third category of approaches uses tripartite graphs, whose set of vertices is typically composed of users, tags, and resources. [PERSON] et al. [15] presented a recommendation method using a user-image-tag model, whose main novelties concern user preference identification on the basis of users' interaction with images and re-ranking social images on the basis of the content. In [16], the authors introduced an interaction tripartite graph, composed of heterogeneous vertices (users, interaction behavior, and content), whose edge weights are tuned by using an attention-driven CNN for recommendation. A tripartite graph--whose set of vertices is composed of users, tweets, and topics--was detailed by [PERSON], [PERSON], and [PERSON] [17] for providing coronavirus pandemic analysis through non-negative matrix factorization and sentiment analysis. Finally, the last group defines the OSN as a hypergraph. In [18], the authors proposed a tensor decomposition approach that guarantees learning via a three-uniform hypergraph. A heterogeneous hypergraph embedding (_LBSN2 Vec++_) was developed by [PERSON] et al. [19] in order to consider complex interactions among users, time, and Points of Interest (POIs) for friendship and location prediction tasks in a location-based social network. [PERSON] et al. [20] developed a hybrid matrix factorization approach exploiting a hypergraph data structure to represent complex interactions in social networks for recommending items. The chosen model was inspired by hypergraph-based approaches, which surely are the most promising ones. In particular, starting form our preliminary previous work [4], \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Ref.** & **Entities** & **Application** \\ \hline \multirow{3}{*}{Graph} & [11] & Multimedia objects, concepts & Multimedia annotation \\ \cline{2-4}  & [12] & Images, users, and tags & Link-based similarity \\ \hline \multirow{3}{*}{Bipartite} & [13] & Users and contents & Influence diffusion \\ \cline{2-4}  & [14] & Users and contents & Social recommendation \\ \hline \multirow{3}{*}{Tripartite} & [15] & Users, tags, and images & Recommendation \\ \cline{2-4}  & [16] & Users, interaction behavior, and tags & Recommendation \\ \cline{2-4}  & [17] & Users, Tweets, and topics & Coronavirus analysis \\ \hline \multirow{3}{*}{Hypergraph} & [18] & Users, tags, and resources & Consensus maximization \\ \cline{2-4}  & [19] & Users, time, and POIs & Location prediction \\ \cline{1-1} \cline{2-4}  & [20] & Users and items & Recommendation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: OSN models.