# NCSAC: Effective Neural Community Search via Attribute-augmented Conductance

arXiv: https://arxiv.org/abs/2511.04712 · HTML: https://mlsanity.com/paper/2511.04712/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2025.

## Built on by (most cited first, 12 of 12)

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- [Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks](https://mlsanity.com/paper/2607.04091/) (2026)
- [Physics-Informed Graph Learning with Uncertainty Awareness for Open-Set Domain Generalization in Fault Diagnosis](https://mlsanity.com/paper/2607.04188/) (2026)
- [A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning](https://mlsanity.com/paper/2607.11107/) (2026)
- [CA-DGCL: Dynamic Graph Continual Learning via Condensation and Attachment](https://mlsanity.com/paper/2607.11112/) (2026)
- [CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning](https://mlsanity.com/paper/2609.25814/) (2026)

## Abstract (arXiv)

Identifying locally dense communities closely connected to the user-initiated query node is crucial for a wide range of applications. Existing approaches either solely depend on rule-based constraints or exclusively utilize deep learning technologies to identify target communities. Therefore, an important question is proposed: can deep learning be integrated with rule-based constraints to elevate the quality of community search? In this paper, we affirmatively address this question by introducing a novel approach called Neural Community Search via Attribute-augmented Conductance, abbreviated as NCSAC. Specifically, NCSAC first proposes a novel concept of attribute-augmented conductance, which harmoniously blends the (internal and external) structural proximity and the attribute similarity. Then, NCSAC extracts a coarse candidate community of satisfactory quality using the proposed attribute-augmented conductance. Subsequently, NCSAC frames the community search as a graph optimization task, refining the candidate community through sophisticated reinforcement learning techniques, thereby producing high-quality results. Extensive experiments on six real-world graphs and ten competitors demonstrate the superiority of our solutions in terms of accuracy, efficiency, and scalability. Notably, the proposed solution outperforms state-of-the-art methods, achieving an impressive F1-score improvement ranging from 5.3% to 42.4%. For reproducibility purposes, the source code is available at https://github.com/longlonglin/ncsac.