# Community Detection Framework Using Deep Learning in Social Media Analysis
## 2 Related Work
### Community Detection Methods  
Community detection is a method to find relationships between individuals (represented by nodes) from graph-structured data [17]. It mainly analyzes the connection between edges and nodes and the size of weights, focusing on the network topology. [PERSON] et al. [18] propose a community detection architecture based on deep learning algorithms, which is implemented by stacking multiple auto-encoders and applying parameter sharing. The method not only performs well in community detection but also achieves remarkable results in link prediction and node classification tasks. However, a limitation of this study is that its model has high complexity and may not be suitable for processing large-scale social network data. Ref. [19] focuses on the edges in a social network and finds the topology structure and important network members through the clustering method. Ref. [7] propose the Node2 Vec method, combining DFS (Depth First, Search) and BFS (Breadth-First, Search) algorithms in order to extract the random walks. Node2 Vec can be applied to different social networks, increasing the adaptability and the flexibility of the graph embedding algorithm. Ref. [20] generalize the undirected graph to the directed graph of the social network, compare the result with traditional analysis tools, and prove that the topological graph has a better accuracy of community detection. Ref. [21] proposed two parameter-free models to detect communities, combining topological information and attribute information. [PERSON] et al. [22] propose a semi-supervised community detection method that utilizes CNNs (convolutional neural networks) in combination with the topology and contextual information of networks. The innovation of this approach lies in its simultaneous use of multiple network attributes, which enhances the accuracy of community detection. However, this method relies on a large amount of labeled data, which may be difficult to obtain in practical applications. [PERSON] et al. [23] introduce a hypergraph-based community detection method for multimedia social networks. By converting hypergraphs into adjacency matrices, they effectively represented the complex relationships between users. This method performs excellently when handling multimedia data, but its computational complexity is high, making it less suitable for large-scale networks. [PERSON] et al. [24] propose a deep learning solution for detecting hate speech on social media in an Internet of Things (IoT) environment. This approach effectively improves the detection accuracy of hate speech by integrating IoT devices with social media analysis systems. However, the study did not adequately address the data synchronization issues between IoT devices and social media data, which could affect the system's real-time performance. The DUDetector framework proposed by [PERSON] and [PERSON] [11] performs exceptionally well in detecting depressed users on Twitter. Experimental results indicate that this framework outperforms existing solutions in identifying depressed users. Nevertheless, this study primarily relies on Twitter data, and its generalizability requires further validation. [PERSON] et al. [25] propose a GNN (Graph Neural Network)-based framework that integrates both node features and network topology, achieving improved performance in community detection tasks. Their approach demonstrates the effectiveness of GNNs in learning expressive representations that can better capture the underlying community structures within social networks.