# A Survey of Community Search Over Big Graphs

arXiv: https://arxiv.org/abs/1904.12539 · HTML: https://mlsanity.com/paper/1904.12539/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 21 of 21)

- [A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications](https://mlsanity.com/paper/2404.14809/) (2024)
- [Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model](https://mlsanity.com/paper/2109.02058/) (2021)
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- [Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed](https://mlsanity.com/paper/2104.03583/) (2021)
- [CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks](https://mlsanity.com/paper/2210.08811/) (2022)
- [Learning Large-Scale MTP2 Gaussian Graphical Models via Bridge-Block Decomposition](https://mlsanity.com/paper/2309.13405/) (2023)
- [CS-TGN: Community Search via Temporal Graph Neural Networks](https://mlsanity.com/paper/2303.08964/) (2023)
- [Scalable Motif Counting for Large-scale Temporal Graphs](https://mlsanity.com/paper/2204.09236/) (2022)
- [Explaining Interactions Between Text Spans](https://mlsanity.com/paper/2310.13506/) (2023)
- [An epistemic approach to model uncertainty in data-graphs](https://mlsanity.com/paper/2109.14112/) (2021)
- [AdaGrid: Adaptive Grid Search for Link Prediction Training Objective](https://mlsanity.com/paper/2203.16162/) (2022)
- [A Survey on Machine Learning Solutions for Graph Pattern Extraction](https://mlsanity.com/paper/2204.01057/) (2022)
- [An Effective Index for Truss-based Community Search on Large Directed Graphs](https://mlsanity.com/paper/2401.10641/) (2024)
- [Fast Butterfly-Core Community Search For Large Labeled Graphs](https://mlsanity.com/paper/2401.10642/) (2024)
- [Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities](https://mlsanity.com/paper/2502.10750/) (2025)
- [ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities](https://mlsanity.com/paper/2504.01122/) (2025)
- [Community Search in Time-dependent Road-social Attributed Networks](https://mlsanity.com/paper/2505.12309/) (2025)
- [PLACE: Prompt Learning for Attributed Community Search in Large Graphs](https://mlsanity.com/paper/2507.05311/) (2025)
- [Beyond Homophily: Community Search on Heterophilic Graphs](https://mlsanity.com/paper/2601.01703/) (2026)
- [DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.08545/) (2026)
- [PreGress: Ranking-Native Pre-training and Prompting for Graph Node Ranking](https://mlsanity.com/paper/2608.09016/) (2026)

## Abstract (arXiv)

With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have a better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.