# CLARE: A Semi-supervised Community Detection Algorithm

arXiv: https://arxiv.org/abs/2210.08274 · HTML: https://mlsanity.com/paper/2210.08274/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2022.

## Built on by (most cited first, 13 of 13)

- [How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension](https://mlsanity.com/paper/2410.05298/) (2024)
- [A Comprehensive Review of Community Detection in Graphs](https://mlsanity.com/paper/2309.11798/) (2023)
- [Repeat-Aware Neighbor Sampling for Dynamic Graph Learning](https://mlsanity.com/paper/2405.17473/) (2024)
- [CSGCL: Community-Strength-Enhanced Graph Contrastive Learning](https://mlsanity.com/paper/2305.04658/) (2023)
- [Robust Stochastic Graph Generator for Counterfactual Explanations](https://mlsanity.com/paper/2312.11747/) (2023)
- [Graph Inverse Style Transfer for Counterfactual Explainability](https://mlsanity.com/paper/2505.17542/) (2025)
- [Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes](https://mlsanity.com/paper/2308.02353/) (2023)
- [A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases](https://mlsanity.com/paper/2412.06212/) (2024)
- [A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty](https://mlsanity.com/paper/2304.04497/) (2023)
- [HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection](https://mlsanity.com/paper/2411.01947/) (2024)
- [New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics](https://mlsanity.com/paper/2504.15927/) (2025)
- [Pre-trained Prompt-driven Semi-supervised Local Community Detection](https://mlsanity.com/paper/2505.12304/) (2025)
- [Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes](https://mlsanity.com/paper/2608.06402/) (2026)

## Abstract (arXiv)

Community detection refers to the task of discovering closely related subgraphs to understand the networks. However, traditional community detection algorithms fail to pinpoint a particular kind of community. This limits its applicability in real-world networks, e.g., distinguishing fraud groups from normal ones in transaction networks. Recently, semi-supervised community detection emerges as a solution. It aims to seek other similar communities in the network with few labeled communities as training data. Existing works can be regarded as seed-based: locate seed nodes and then develop communities around seeds. However, these methods are quite sensitive to the quality of selected seeds since communities generated around a mis-detected seed may be irrelevant. Besides, they have individual issues, e.g., inflexibility and high computational overhead. To address these issues, we propose CLARE, which consists of two key components, Community Locator and Community Rewriter. Our idea is that we can locate potential communities and then refine them. Therefore, the community locator is proposed for quickly locating potential communities by seeking subgraphs that are similar to training ones in the network. To further adjust these located communities, we devise the community rewriter. Enhanced by deep reinforcement learning, it suggests intelligent decisions, such as adding or dropping nodes, to refine community structures flexibly. Extensive experiments verify both the effectiveness and efficiency of our work compared with prior state-of-the-art approaches on multiple real-world datasets.