Research on Community Discovery Techniques Based on Knowledge Distillation

Graph Neural Networks (GNN) have found broad applications in diverse domains, including community detection, classification of nodes, and prediction of links. Unfortunately, in the real world, many networks usually have the problem of insufficient connection and label information, and GNN mainly relies on graph-based feature propagation and smoothing operations for graph task analysis. In the absence of connection and label information, the feature propagation operation of GNN will be greatly affected., so these problems pose an obstacle to the performance of GNN. In this context, we introduce an innovative model for community discovery based on knowledge distillation. The objective is to extract information on community structures from intricate networks and apply it to a student model, thus improving the effectiveness of community detection tasks.