Pre-trained Prompt-driven Semi-supervised Local Community Detection
This work addresses efficiency issues in community detection for network analysis, but it appears incremental as it adapts an existing paradigm to a specific task.
The paper tackles the problem of time-consuming semi-supervised local community detection by proposing a method based on the 'pre-train, prompt' paradigm, which improves both community quality and efficiency on five real-world datasets.
Semi-supervised local community detection aims to leverage known communities to detect the community containing a given node. Although existing semi-supervised local community detection studies yield promising results, they suffer from time-consuming issues, highlighting the need for more efficient algorithms. Therefore, we apply the "pre-train, prompt" paradigm to semi-supervised local community detection and propose the Pre-trained Prompt-driven Semi-supervised Local community detection method (PPSL). PPSL consists of three main components: node encoding, sample generation, and prompt-driven fine-tuning. Specifically, the node encoding component employs graph neural networks to learn the representations of nodes and communities. Based on representations of nodes and communities, the sample generation component selects known communities that are structurally similar to the local structure of the given node as training samples. Finally, the prompt-driven fine-tuning component leverages these training samples as prompts to guide the final community prediction. Experimental results on five real-world datasets demonstrate that PPSL outperforms baselines in both community quality and efficiency.