# Community Detection Framework Using Deep Learning in Social Media Analysis
## 3 Proposed Community Detection Framework
### Community Detection and Visualization  
Community visualization in community detection refers to the process of visually representing the community structure of a network. This involves creating graphical representations of the network where each community is represented by a distinct color, shape or label. The visual representation can help to identify patterns and structures in the network, such as the size and composition of each community, the connections between communities, and the central nodes within each community. The visualization can be used to display the distribution of edges between nodes within and between communities, as well as the hierarchical structure of communities within a larger network. Community visualization provides a valuable tool for analyzing and understanding the structure and dynamics of complex networks. In the community detection module, we adopt ClusterNet, which can deal with both learning and optimization processes in one end-to-end model. The standard way of solving learning and optimization problems is to solve the learning problem first and then solve the optimization problem, which will lead to the failure of collaborative improvement with the learning results and downstream optimization tasks. ClusterNet can backward pass the loss of optimization task to the learning task to improve the optimization performance. The method also introduces link prediction into the community detection problem. Link prediction is firstly utilized to discover unknown edges in the network. Community detection is then performed on the restored network. The architecture and training process consists of four steps, as shown in Figure 2: GCN node embedding, K-means node clustering, decision making, and backpropagation optimization objective value. 1. GCN node embedding: The first step is to use a GCN to embed the nodes of the graph into a continuous vector space. The goal is to train the GCN to produce task-specific embeddings that capture important features of the graph relevant to the optimization problem at hand. Specifically, the node feature in the GCN node embedding is the computation result of Section 3.2. 2. K-means node clustering: The method is used due to its efficiency and effectiveness in segmenting large datasets into distinct clusters based on feature similarity. This method is particularly beneficial in scenarios where a clear distinction is required among the detected communities in social network data. Once the nodes have been embedded, a differentiable version of K-means clustering is applied to group similar nodes together into clusters. This produces a soft assignment of each node to each cluster, along with the cluster centers in the embedding space. 3. Decision making: Interpret clustering as optimization solution. The resulting soft clustering allows each data point to belong to multiple clusters with varying degrees of membership, offers a flexible interpretation of data relationships in graph-based analyses. This method can be viewed as providing an approximate solution to optimization problems on a graph, encompassing scenarios like community detection or facility location. By grouping similar nodes together into clusters, we can identify meaningful structures in the graph that correspond to real-world phenomena. The clustering result based on GCN node embedding, learning, and optimization is the community detection result. 4. Backpropagate optimization objective value: Finally, the optimization objective value is backpropagated through the GCN network to update the embeddings of each node. Specifically, we compute an objective function that measures how well our soft clustering approximates the true solution to our optimization problem, and use this objective function to update our model parameters via gradient descent. The GCN network learns to produce task-specific embeddings that better align with the clustering objectives. This process is typically repeated for several iterations until the embeddings converge to a stable solution.