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https://github.com/FanzhenLiu/Awesome-Deep-Community-Detection

Deep and conventional community detection related papers, implementations, datasets, and tools.
https://github.com/FanzhenLiu/Awesome-Deep-Community-Detection
List: Awesome-Deep-Community-Detection
awesome community-detection data-mining deep-learning deep-neural-network graph-clustering graph-embedding graph-neural-network graph-neural-networks network-embedding network-representation-learning survey
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Deep and conventional community detection related papers, implementations, datasets, and tools.
Host: GitHub
URL: https://github.com/FanzhenLiu/Awesome-Deep-Community-Detection
Owner: FanzhenLiu
License: mit
Created: 2020-06-24T08:48:53.000Z (over 4 years ago)
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Last Pushed: 2023-12-31T01:32:33.000Z (12 months ago)
Last Synced: 2024-10-09T00:04:29.517Z (2 months ago)
Topics: awesome, community-detection, data-mining, deep-learning, deep-neural-network, graph-clustering, graph-embedding, graph-neural-network, graph-neural-networks, network-embedding, network-representation-learning, survey
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Metadata Files:
- Readme: README.md
- License: LICENSE
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ultimate-awesome - Awesome-Deep-Community-Detection - Deep and conventional community detection related papers, implementations, datasets, and tools. (Other Lists / Monkey C Lists)
README

        # Awesome Deep Community Detection

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) ![GitHub stars](https://img.shields.io/github/stars/FanzhenLiu/awesome-deep-community-detection?color=yellow&label=Stars) ![GitHub forks](https://img.shields.io/github/forks/FanzhenLiu/awesome-deep-community-detection?color=blue&label=Forks) 

A collection of papers, implementations, datasets, and tools for deep and non-deep community detection.

- [Awesome Deep Community Detection](#awesome-Deep-Community-Detection)

  - [Survey](#survey)

  - [Convolutional Networks-based Community Detection](#convolutional-networks-based-community-detection)

  	- [CNN-based Community Detection](#cnn-based-community-detection)

  	- [GCN-based Community Detection](#gcn-based-community-detection)

  - [Graph Attention Network-based Community Detection](#graph-attention-network-based-community-detection)

  - [Generative Adversarial Network-based Community Detection](#graph-adversarial-network-based-community-detection)

  - [Autoencoder-based Community Detection](#autoencoder-based-community-detection)

  - [Other Deep Learning-based Community Detection](#other-deep-learning-based-community-detection)

  - [Non-Deep Learning-based Communtiy Detection](#non-deep-learning-based-communtiy-detection)

  - [Datasets](#datasets)

  - [Tools](#tools)  

----------

## Traditional Methods _VS._ Deep Learninig-based Methods

![taxonomy](taxonomy.png)

----------

## A Timeline of Community Detection Development

![timeline](timeline.png)

----------

## Survey

| Paper Title | Venue | Year | Materials | 

| ---- | :----: | :----: | :----: | 

| A comprehensive survey on community detection with deep learning | IEEE TNNLS | 2022 | [[Paper](https://ieeexplore.ieee.org/document/9732192)] 
 [[Report](https://www.aminer.cn/research_report/60da8c5f30e4d5752f50e7af)] 
 [[Supplementary](https://www.researchgate.net/publication/359222598_Supplementary_DeepCommunityDetectionSurveypdf)]|

| A survey of community detection approaches: From statistical modeling to deep learning | IEEE TKDE | 2021 | [[Paper](https://ieeexplore.ieee.org/abstract/document/9511798)]|

| Deep learning for community detection: Progress, challenges and opportunities | IJCAI | 2020 | [[Paper](https://www.ijcai.org/Proceedings/2020/0693.pdf)] 
[[Report](https://cloud.tencent.com/developer/article/1632305)]| 

| A survey of community detection methods in multilayer networks | Data Min. Knowl. Discov. | 2020 | [[Paper](https://link.springer.com/article/10.1007/s10618-020-00716-6)] |

| Community detection in node-attributed social networks: A survey | Comput. Sci. Rev. | 2020 | [[Paper](https://www.sciencedirect.com/science/article/pii/S1574013720303865)] |

| Community detection in networks: A multidisciplinary review | J. Netw. Comput. Appl. | 2018| [[Paper](https://www.sciencedirect.com/science/article/pii/S1084804518300560)] |

| Community discovery in dynamic networks: A survey | ACM Comput. Surv. | 2018 | [[Paper](https://dl.acm.org/doi/10.1145/3172867)] |

| Evolutionary computation for community detection in networks: A review | IEEE TEVC | 2018 | [[Paper](https://ieeexplore.ieee.org/document/8004509)] |

| Metrics for community analysis: A survey | ACM Comput. Surv. | 2017 | [[Paper](https://dl.acm.org/doi/10.1145/3091106)] |

| Network community detection: A review and visual survey | Preprint | 2017 | [[Paper](https://arxiv.org/abs/1708.00977)] |

| Community detection in networks: A user guide | Phys. Rep. | 2016 | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0370157316302964)] |

| Community detection in social networks | WIREs Data Min. Knowl. Discov. | 2016 | [[Paper](https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1178)]|

| Overlapping community detection in networks: The state-of-the-art and comparative study| ACM Comput. Surv. | 2013 | [[Paper](https://dl.acm.org/doi/10.1145/2501654.2501657)] |

| Clustering and community detection in directed networks: A survey | Phys. Rep. | 2013 | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0370157313002822)] |

| Community detection in graphs | Phys. Rep. | 2010 | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0370157309002841)] |

----------

## Convolutional Networks-based Community Detection

### CNN-based Community Detection

| Paper Title | Venue | Year | Method | Materials | 

| ---- | :----: | :----: | :----: | :----: | 

|Inductive representation learning via CNN for partially-unseen attributed networks | IEEE TNSE | 2021 | IEPAN | [[Paper](https://ieeexplore.ieee.org/abstract/document/9314087)] |

|A deep learning approach for semi-supervised community detection in online social networks | Knowl.-Based Syst. | 2021 | SparseConv2D | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0950705121006079)] |

|Edge classification based on convolutional neural networks for community detection in complex network | Physica A | 2020 | ComNet-R | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0378437120304271)] |

|A deep learning based community detection approach | SAC | 2019 | SparseConv | [[Paper](https://doi.org/10.1145/3297280.3297574)] |

|Deep community detection in topologically incomplete networks | Physica A | 2017 | Xin _et al._ | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0378437116308342)] |

### GCN-based Community Detection 

| Paper Title | Venue | Year | Method | Materials | 

| ---- | :----: | :----: | :----: | :----: | 

|Complex exponential graph convolutional networks | Inf. Sci. | 2023 | CEGCN | [[Paper](https://doi.org/10.1016/j.ins.2023.119041)] [[Code](https://github.com/Daffominga/cegcn)] |

|Community detection based on community perspective and graph convolutional network| Expert Syst. Appl. | 2023 | CPGC | [[Paper](https://www.sciencedirect.com/science/article/pii/S0957417423012502)] |

|Heterogeneous question answering community detection based on graph neural network | Inf. Sci. | 2023 | HCDBG | [[Paper](https://doi.org/10.1016/j.ins.2022.10.126)] |

|Overlapping community detection on complex networks with graph convolutional networks | Comput. Commun. | 2023 | CDMG | [[Paper](https://doi.org/10.1016/j.comcom.2022.12.008)] |

|Deep MinCut: Learning node embeddings from detecting communities | Pattern Recognit. | 2022 | DMC | [[Paper](https://doi.org/10.1016/j.patcog.2022.109126)] |

|End-to-end modularity-based community co-partition in bipartite networks| CIKM | 2022 | BiCoN+GCN | [[Paper](https://dl.acm.org/doi/10.1145/3511808.3557309)] |

|CLARE: A semi-supervised community detection algorithm | KDD | 2022 | CLARE | [[Paper](https://dl.acm.org/doi/10.1145/3534678.3539370)] [[Code](https://github.com/FDUDSDE/KDD2022CLARE)] |

|Efficient graph convolution for joint node representation learning and clustering  | WSDM | 2022 | GCC | [[Paper](https://dl.acm.org/doi/10.1145/3488560.3498533)] [[Code](https://github.com/chakib401/graph_convolutional_clustering)] |

|Geometric graph representation learning via maximizing rate reduction | WWW | 2022 | $G^2R$ | [[Paper](https://dl.acm.org/doi/pdf/10.1145/3485447.3512170)] [[Code](https://github.com/ahxt/G2R)] | 

| RepBin: Constraint-based graph representation learning for metagenomic binning | AAAI | 2022 | RepBin | [[Paper](https://www.aaai.org/AAAI22Papers/AAAI-4979.XueH.pdf)] [[Code](https://github.com/xuehansheng/RepBin)] |

|SSSNET: Semi-supervised signed network clustering | SDM | 2022 | SSSNET | [[Paper](https://arxiv.org/pdf/2110.06623.pdf)] [[Code](https://github.com/SherylHYX/SSSNET_Signed_Clustering)] | 

|Learning Guarantees for Graph Convolutional Networks on The Stochastic Block Model | ICLR | 2022 | GCN-SBM | [[Paper](https://openreview.net/forum?id=dpXL6lz4mOQ)] |

|When convolutional network meets temporal heterogeneous graphs: An effective community detection method | IEEE TKDE| 2021 | THGCN | [[Paper](https://ieeexplore.ieee.org/document/9479741)] |

|Multi-view contrastive graph clustering | NIPS | 2021 | MCGC | [[paper](https://proceedings.neurips.cc/paper/2021/file/10c66082c124f8afe3df4886f5e516e0-Paper.pdf)] [[Code](https://github.com/Panern/MCGC)] |

|Graph debiased contrastive learning with joint representation clustering | IJCAI | 2021 | Zhao _et al._ | [[Paper](https://www.ijcai.org/proceedings/2021/0473.pdf)] | 

|Spectral embedding network for attributed graph clustering | Neural Netw. | 2021 | SENet | [[Paper](https://www.sciencedirect.com/science/article/pii/S0893608021002227)] | 

|Unsupervised learning for community detection in attributed networks based on graph convolutional network | Neurocomputing | 2021 | SGCN | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231221008110)] |

|Adaptive graph encoder for attributed graph embedding | KDD | 2020 | AGE | [[Paper](https://dl.acm.org/doi/10.1145/3394486.3403140)][[Code](https://github.com/thunlp/AGE)] |

|CommDGI: Community detection oriented deep graph infomax | CIKM | 2020 | CommDGI | [[Paper](https://dl.acm.org/doi/abs/10.1145/3340531.3412042)] | 

|Going deep: Graph convolutional ladder-shape networks | AAAI | 2020 | GCLN | [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/5673/5529)] |

|Independence promoted graph disentangled networks | AAAI | 2020 | IPGDN | [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/5929/5785)] |

|Supervised community detection with line graph neural networks | ICLR | 2019 | LGNN | [[Paper](https://openreview.net/pdf?id=H1g0Z3A9Fm)][[Code](https://github.com/zhengdao-chen/GNN4CD)] |

|Graph convolutional networks meet Markov random fields: Semi-supervised community detection in attribute networks | AAAI | 2019 | MRFasGCN | [[Paper](https://www.aaai.org/ojs/index.php/AAAI/article/view/3780/3658)] |

|Overlapping community detection with graph neural networks | DLG Workshop, KDD | 2019 | NOCD | [[Paper](https://deep-learning-graphs.bitbucket.io/dlg-kdd19/accepted_papers/DLG_2019_paper_3.pdf)][[Code](https://github.com/shchur/overlapping-community-detection)] |

|Attributed graph clustering via adaptive graph convolution | IJCAI | 2019 | AGC | [[Paper](https://www.ijcai.org/Proceedings/2019/0601.pdf)][[Code](https://github.com/karenlatong/AGC-master)] |

|CayleyNets: Graph convolutional neural networks with complex rational spectral filters | IEEE TSP | 2019 | CayleyNets | [[Paper](https://ieeexplore.ieee.org/document/8521593)][[Code](https://github.com/amoliu/CayleyNet)] |

----------

## Graph Attention Network-based Community Detection

| Paper Title | Venue | Year | Method | Materials | 

| ---- | :----: | :----: | :----: | :----: | 

|CSAT: Contrastive sampling-aggregating transformer for community detection in attribute-missing networks | IEEE TCSS | 2023 | CSAT | [[Paper](https://ieeexplore.ieee.org/document/10210121)] |

|A graph-enhanced attention model for community detection in multiplex networks | Expert Syst. Appl. | 2023 | GEAM | [[Paper](https://www.sciencedirect.com/science/article/pii/S0957417423010540)][[Code](https://github.com/HustMinsLab/GEAM)] |

|Hierarchical attention network for attributed community detection of joint representation | Neural Comput. Appl. | 2022 | HiAN | [[Paper](https://doi.org/10.1007/s00521-021-06723-y)] |

|Detecting communities from heterogeneous graphs: A context path-based graph neural network model | CIKM | 2021 |  CP-GNN  | [[Paper](https://arxiv.org/pdf/2109.02058.pdf)][[Code](https://github.com/RManLuo/CP-GNN)] | 

|HDMI: High-order deep multiplex infomax | WWW | 2021 | HDMI | [[Paper](https://dl.acm.org/doi/fullHtml/10.1145/3442381.3449971)][[Code](https://github.com/baoyujing/HDMI)] |

|Self-supervised heterogeneous graph neural network with co-contrastive learning | KDD | 2021 | HeCo | [[Paper](https://dl.acm.org/doi/10.1145/3447548.3467415)][[Code](https://github.com/liun-online/HeCo)] |

|Unsupervised attributed multiplex network embedding | AAAI | 2020 | DMGI | [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/5985)][[Code](https://github.com/pcy1302/DMGI)] |

|MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding | WWW | 2020 | MAGNN | [[Paper](https://dl.acm.org/doi/abs/10.1145/3366423.3380297)] [[Code](https://github.com/cynricfu/MAGNN)] |

----------

## Graph Adversarial Network-based Community Detection

| Paper Title | Venue | Year | Method | Materials | 

| ---- | :----: | :----: | :----: | :----: |  

|CANE: Community-aware network embedding via adversarial training |Knowl. Inf. Syst. | 2021 | CANE | [[Paper](https://link.springer.com/article/10.1007%2Fs10115-020-01521-9)] | 

|Self-training enhanced: Network embedding and overlapping community detection with adversarial learning | IEEE TNNLS | 2021 | ACNE 
 ACNE-ST 
 | [[Paper](https://ieeexplore.ieee.org/document/9451542)] |

|Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks |ICDM | 2021 | ABC | [[Paper](https://ieeexplore.ieee.org/document/9679159)] | 

|SEAL: Learning heuristics for community detection with generative adversarial networks | KDD | 2020 | SEAL | [[Paper](https://dl.acm.org/doi/abs/10.1145/3394486.3403154)][[Code](https://github.com/yzhang1918/kdd2020seal)] |

|Multi-class imbalanced graph convolutional network learning | IJCAI | 2020 | DR-GCN | [[Paper](https://www.ijcai.org/proceedings/2020/0398.pdf)] |

|JANE: Jointly adversarial network embedding | IJCAI | 2020| JANE | [[Paper](https://www.ijcai.org/Proceedings/2020/0192.pdf)] |

|ProGAN: Network embedding via proximity generative adversarial network | KDD | 2019 | ProGAN | [[Paper](https://dl.acm.org/doi/pdf/10.1145/3292500.3330866)] |

|CommunityGAN: Community detection with generative adversarial nets | WWW | 2019 | CommunityGAN | [[Paper](https://dl.acm.org/doi/abs/10.1145/3308558.3313564)][[Code](https://github.com/SamJia/CommunityGAN)] |

----------

## Autoencoder-based Community Detection

| Paper Title | Venue | Year | Method | Materials | 

| ---- | :----: | :----: | :----: | :----: |

|A graph convolutional fusion model for community detection in multiplex networks | Data Min. Knowl. Discov. | 2023 | GCFM | [[Paper](https://doi.org/10.1007/s10618-023-00932-w)] |

|A novel network core structure extraction algorithm utilized variational autoencoder for community detection | Expert Syst. Appl. | 2023 | CSEA | [[Paper](https://doi.org/10.1016/j.eswa.2023.119775)][[Code](https://github.com/PeterWana/CSEA)] |

|Community detection based on unsupervised attributed network embedding | Expert Syst. Appl. | 2023 | CDBNE | [[Paper](https://doi.org/10.1016/j.eswa.2022.118937)][[Code](https://github.com/xidizxc/CDBNE)] |

|Exploring temporal community structure via network embedding | IEEE TCYB | 2022 | VGRGMM | [[Paper](https://ieeexplore.ieee.org/abstract/document/9768181)]|

|Graph community infomax | ACM TKDD | 2022 | GCI | [[Paper](https://dl.acm.org/doi/10.1145/3480244)] |

|Multi-modal non-Euclidean brain network analysis with community detection and convolutional autoencoder | IEEE TETCI | 2022 | M2CDCA | [[Paper](https://ieeexplore.ieee.org/document/9773106)] |

|Deep neighbor-aware embedding for node clustering in attributed graphs | Pattern Recognit. | 2022 | DNENC | [[Paper](https://doi.org/10.1016/j.patcog.2021.108230)] |

|Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder | Inf. Sci. | 2022 | SSGCAE | [[Paper](https://doi.org/10.1016/j.ins.2022.07.036)] |

|A weighted network community detection algorithm based on deep learning | Appl. Math. Comput. | 2021 | WCD | [[Paper](https://www.sciencedirect.com/science/article/pii/S0096300321000606)] |

| DNC: A deep neural network-based clustering-oriented network embedding algorithm | J. Netw. Comput. Appl. | 2021 | DNC | [[Paper](https://www.sciencedirect.com/science/article/pii/S1084804520303209)] |

|Boosting nonnegative matrix factorization based community detection with graph attention auto-encoder | IEEE TBD | 2021 | NMFGAAE | [[Paper](https://ieeexplore.ieee.org/abstract/document/9512416)]|

|Self-supervised graph convolutional network for multi-view clustering | IEEE TMM | 2021 | SGCMC | [[Paper](https://ieeexplore.ieee.org/document/9472979)] |

|Graph embedding clustering: Graph attention auto-encoder with cluster-specificity distribution | Neural Netw. | 2021 | GEC-CSD | [[Paper](https://www.sciencedirect.com/science/article/pii/S0893608021002008)][[Code](https://github.com/xdweixia/SGCMC)] |

|An evolutionary autoencoder for dynamic community detection | Sci. China Inf. Sci. | 2020 |  sE-Autoencoder  | [[Paper](https://link.springer.com/article/10.1007/s11432-020-2827-9)] |

|Stacked autoencoder-based community detection method via an ensemble clustering framework | Inf. Sci. | 2020 | CDMEC | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S002002552030270X)] |

|Community-centric graph convolutional network for unsupervised community detection | IJCAI | 2020 | GUCD | [[Paper](https://www.ijcai.org/Proceedings/2020/0486.pdf)] |

|Structural deep clustering network |  WWW | 2020 | SDCN | [[Paper](https://dl.acm.org/doi/10.1145/3366423.3380214)][[Code](https://github.com/bdy9527/SDCN)] |

|One2Multi graph autoencoder for multi-view graph clustering | WWW | 2020 | One2Multi | [[Paper](https://dl.acm.org/doi/10.1145/3366423.3380079)][[Code](https://github.com/googlebaba/WWW2020-O2MAC)] |

|Multi-view attribute graph convolution networks for clustering | IJCAI | 2020 | MAGCN | [[Paper](https://www.ijcai.org/Proceedings/2020/0411.pdf)] |

|Deep multi-graph clustering via attentive cross-graph association | WSDM | 2020 | DMGC | [[Paper](https://dl.acm.org/doi/abs/10.1145/3336191.3371806)][[Code](https://github.com/flyingdoog/DMGC)] |

|Effective decoding in graph auto-encoder using triadic closure | AAAI | 2020 | TGA 
 TVGA 
 | [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/5437/5293)] |

|Graph representation learning via ladder gamma variational autoencoders | AAAI | 2020 | LGVG | [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/6013/5869)] |

|High-performance community detection in social networks using a deep transitive autoencoder | Inf. Sci. | 2019 |  Transfer-CDDTA  | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0020025519303251)] |

|Attributed graph clustering: A deep attentional embedding approach | IJCAI | 2019 | DAEGC | [[Paper](https://www.ijcai.org/Proceedings/2019/0509.pdf)] |

|Stochastic blockmodels meet graph neural networks | ICML | 2019 | DGLFRM | [[Paper](http://proceedings.mlr.press/v97/mehta19a/mehta19a.pdf)][[Code](https://github.com/nikhil-dce/SBM-meet-GNN)] |

|Variational graph embedding and clustering with laplacian eigenmaps | IJCAI | 2019 | VGECLE | [[Paper](https://www.ijcai.org/Proceedings/2019/0297.pdf)] |

|Optimizing variational graph autoencoder for community detection | BigData | 2019 | VGAECD-OPT | [[Paper](https://ieeexplore.ieee.org/abstract/document/9006123)] |

|Integrative network embedding via deep joint reconstruction | IJCAI | 2018 | UWMNE | [[Paper](https://www.ijcai.org/Proceedings/2018/0473.pdf)] |

|Deep attributed network embedding | IJCAI | 2018 | DANE | [[Paper](https://www.ijcai.org/Proceedings/2018/0467.pdf)][[Code](https://github.com/gaoghc/DANE)] |

|Deep network embedding for graph representation learning in signed networks | IEEE TCYB | 2018 | DNE-SBP | [[Paper](https://ieeexplore.ieee.org/document/8486671)][[Code](https://github.com/shenxiaocam/Deep-network-embedding-for-graph-representation-learning-in-signed-networks)] |

|DFuzzy: A deep learning-based fuzzy clustering model for large graphs | Knowl. Inf.  Syst. | 2018 | DFuzzy | [[Paper](https://link.springer.com/article/10.1007/s10115-018-1156-3)] |

|Learning community structure with variational autoencoder | ICDM | 2018 | VGAECD | [[Paper](https://ieeexplore.ieee.org/document/8594831)] |

|Adversarially regularized graph autoencoder for graph embedding | IJCAI | 2018 | ARGA 
 ARVGA 
 | [[Paper](https://www.ijcai.org/Proceedings/2018/0362.pdf)][[Code](https://github.com/Ruiqi-Hu/ARGA)]| 

|BL-MNE: Emerging heterogeneous social network embedding through broad learning with aligned autoencoder | ICDM | 2017 | DIME | [[Paper](https://doi.org/10.1109/ICDM.2017.70)][[Code](http://www.ifmlab.org/files/code/Aligned-Autoencoder.zip)] |

|MGAE: Marginalized graph autoencoder for graph clustering | CIKM | 2017 | MGAE | [[Paper](https://dl.acm.org/doi/10.1145/3132847.3132967)][[Code](https://github.com/FakeTibbers/MGAE)] |

|Graph clustering with dynamic embedding | Preprint | 2017 | GRACE | [[Paper](https://arxiv.org/abs/1712.08249)] | 

|Modularity based community detection with deep learning | IJCAI | 2016 | semi-DRN | [[Paper](https://www.ijcai.org/Proceedings/16/Papers/321.pdf)][[Code](http://yangliang.github.io/code/DC.zip)] |

|Deep neural networks for learning graph representations | AAAI | 2016 | DNGR | [[Paper](https://dl.acm.org/doi/10.5555/3015812.3015982)] |

|Learning deep representations for graph clustering | AAAI | 2014 | GraphEncoder | [[Paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8527/8571)][[Code](https://github.com/quinngroup/deep-representations-clustering)] |

----------

## Other Deep Learning-based Community Detection 

| Paper Title | Venue | Year | Method | Materials | 

| ---- | :----: | :----: | :----: | :----: | 

| Contrastive deep nonnegative matrix factorization for community detection | ICASSP | 2024 | CDNMF | [[Paper](https://arxiv.org/abs/2311.02357)][[Code](https://github.com/6lyc/CDNMF)] |

|A hyperbolic approach for learning communities on graphs | Data Min. Knowl. Discov. | 2023 | RComE | [[Paper](https://doi.org/10.1007/s10618-022-00902-8)][[Code](https://www.github.com/tgeral68/HyperbolicGraphAndGMM)] |

|Deep alternating non-negative matrix factorisation | Knowl.-Based Syst. | 2022 |  DA-NMF | [[Paper](https://doi.org/10.1016/j.knosys.2022.109210)] |

|CGC: Contrastive Graph Clustering for Community Detection and Tracking | WWW | 2022 | CGC | [[Paper](https://dl.acm.org/doi/10.1145/3485447.3512160)] |

|Deep graph clustering via dual correlation reduction | AAAI | 2022 | DCRN | [[Paper](https://www.aaai.org/AAAI22Papers/AAAI-5928.LiuY.pdf)] [[Code](https://github.com/yueliu1999/DCRN)] |

|Cluster-aware heterogeneous information network embedding | WSDM | 2022 | VaCA-HINE | [[Paper](https://dl.acm.org/doi/abs/10.1145/3488560.3498385)] |

|Graph filter-based multi-view attributed graph clustering | IJCAI | 2021 | MvAGC | [[Paper](https://www.ijcai.org/proceedings/2021/0375.pdf)] [[Code](https://github.com/sckangz/MvAGC)] |

|A deep learning framework for self-evolving hierarchical community detection | CIKM | 2021 | ReinCom | [[Paper](https://dl.acm.org/doi/10.1145/3459637.3482223)] |

|Unsupervised learning of joint embeddings for node representation and community detection | ECML-PKDD | 2021 | J-ENC | [[Paper](https://link.springer.com/chapter/10.1007/978-3-030-86520-7_2)] |

|Community detection based on modularized deep nonnegative matrix factorization | Int. J. Pattern Recognit. Artif. Intell. | 2020 | MDNMF | [[Paper](https://www.worldscientific.com/doi/abs/10.1142/S0218001421590060)] |

|Deep autoencoder-like nonnegative matrix factorization for community detection | CIKM | 2018 | DANMF | [[Paper](https://dl.acm.org/doi/10.1145/3269206.3271697)][[Code](https://github.com/benedekrozemberczki/DANMF)] |

|Community discovery in networks with deep sparse filtering | Pattern Recognit. | 2018 | DSFCD | [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S003132031830116X)] |

|A non-negative symmetric encoder-decoder approach for community detection | CIKM | 2017 | Sun _et al._ | [[Paper](https://dl.acm.org/doi/abs/10.1145/3132847.3132902)] |

----------

## Non-Deep Learning-based Communtiy Detection

| Paper Title | Venue | Year | Method | Materials |

| ---- | :----: | :----: | :----: | :----: |

| CataBEEM: Integrating latent interaction categories in node-wise community detection models for network data | ICML | 2023 | CataBEEM | [[Paper](https://proceedings.mlr.press/v202/zhang23h.html)][[Code](https://github.com/YuhuaZhang1995/CataBEEM)] |

|Nonnegative matrix factorization based on node centrality for community detection | ACM TKDD | 2023 | NCNMF | [[Paper](https://dl.acm.org/doi/full/10.1145/3578520)][[Code](https://github.com/wowoHead/NCNMF)] |

|Dual structural consistency preserving community detection on social networks | IEEE TKDE | 2023 | DSCPCD | [[Paper](https://doi.org/10.1109/TKDE.2022.3230502)][[Code](https://github.com/wyy-cs/DSCPCD)] |

|Symmetry and graph bi-regularized non-negative matrix factorization for precise community detection | IEEE TASAE | 2023 | B-NMF | [[Paper](https://doi.org/10.1109/TASE.2023.3240335)]|

|Multi-view metro station clustering based on passenger flows: A functional data-edged network community detection approach | Data Min. Knowl. Discov. | 2023 | F²MVNCD | [[Paper](https://doi.org/10.1007/s10618-023-00916-w)] |

|Multiplex network community detection algorithm based on motif awareness | Knowl.-Based Syst. | 2023| CDMA | [[Paper](https://doi.org/10.1016/j.knosys.2022.110136)] |

|Community detection via autoencoder-like nonnegative tensor decomposition | IEEE TNNLS | 2022 | ANTD | [[Paper](https://ieeexplore.ieee.org/document/9904739)] |

|Graph regularized nonnegative matrix factorization for community detection in attributed networks| IEEE TNSE | 2022 | AGNMF-AN | [[Paper](https://ieeexplore.ieee.org/document/9904900)]

|Modeling and detecting communities in node attributed networks | IEEE TKDE | 2022 | CRSBM | [[Paper](https://ieeexplore.ieee.org/document/9852668)] |

|The trade-off between topology and content in community detection: An adaptive encoder-decoder-based NMF approach | Expert Syst. Appl. | 2022 | ANMF | [[Paper](https://doi.org/10.1016/j.eswa.2022.118230)] |

|Community detection in subspace of attribute | Inf. Sci. | 2022 | SOA | [[Paper](https://doi.org/10.1016/j.ins.2022.04.047)] |

|Explainability in graph data science: Interpretability, replicability, and reproducibility of community detection | IEEE Signal Process. Mag. | 2022 | --| [[Paper](https://ieeexplore.ieee.org/abstract/document/9810084)] |

|Differentially private community detection for stochastic block models | ICML | 2022 | Seif _et al._ | [[Paper](http://128.84.4.18/abs/2202.00636)] |

|Community detection in multiplex networks based on evolutionary multi-task optimization and evolutionary clustering ensemble | IEEE TEVC | 2022 | BSMCD | [[Paper](https://ieeexplore.ieee.org/document/9802693)] |

|Fine-grained attributed graph clustering | SDM | 2022 | FGC | [[Paper](https://epubs.siam.org/doi/epdf/10.1137/1.9781611977172.42)] [[Code](https://github.com/sckangz/FGC)] |

|HB-DSBM: Modeling the dynamic complex networks from community level to node level | IEEE TNNLS | 2022 | HB-DSBM | [[Paper](https://ieeexplore.ieee.org/document/9721420)]|

|PMCDM: Privacy-preserving multiresolution community detection in multiplex networks | Knowl.-Based Syst. | 2022 | PMCDM | [[Paper](https://doi.org/10.1016/j.knosys.2022.108542)] |

|Rearranging 'indivisible' blocks for community detection | IEEE TKDE | 2022 | RaidB | [[Paper](https://ieeexplore.ieee.org/document/9771068)] |

|Information diffusion-aware likelihood maximization optimization for community detection | Inf. Sci. | 2022 | EM-CD 
 L-Louvain 
 | [[Paper](https://www.sciencedirect.com/science/article/pii/S0020025522003334)] |

|Community detection in partially observable social networks | ACM TKDD | 2022 | KroMFac | [[Paper](https://dl.acm.org/doi/abs/10.1145/3461339)] |

|Diverse and experienced group discovery via hypergraph clustering | SDM | 2022 | Amburg _et al._ | [[Paper](https://epubs.siam.org/doi/epdf/10.1137/1.9781611977172.17)] [[Code](https://github.com/ilyaamburg/fair-clustering-for-diverse-and-experienced-groups)] |

|Community detection in graph: An embedding method | IEEE TNSE | 2022 | SENMF | [[Paper](https://ieeexplore.ieee.org/abstract/document/9626627)] | 

|Community detection using local group assimilation | Expert Syst. Appl. | 2022| LGA | [[Paper](https://www.sciencedirect.com/science/article/pii/S0957417422010600)] |

|Identifying Early Warning Signals from News Using Network Community Detection | AAAI | 2022| Le Vine _et al._ | [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/21503/21252)] |

|Residual2Vec: Debiasing graph embedding with random graphs | NIPS | 2021 | residual2vec | [[Paper](https://proceedings.neurips.cc/paper/2021/file/ca9541826e97c4530b07dda2eba0e013-Paper.pdf)] [[Code](https://github.com/skojaku/residual2vec)] |

|Streaming belief propagation for community detection | NIPS | 2021 | StSBM | [[Paper](https://proceedings.neurips.cc/paper/2021/file/e2a2dcc36a08a345332c751b2f2e476c-Paper.pdf)] |

|Triangle-aware spectral sparsifiers and community detection | KDD | 2021 | Sotiropoulos _et al._ | [[Paper](https://dl.acm.org/doi/10.1145/3447548.3467260)] [[Code](https://www.dropbox.com/s/0p0ybkpx19jt3ii/codeKDDTriangleAware.zip?dl=0)] |

|Self-guided community detection on networks with missing edges | IJCAI | 2021 | SGCD | [[Paper](https://www.ijcai.org/proceedings/2021/0483.pdf)] |

|Effective and scalable clustering on massive attributed graphs | WWW | 2021 | ACMin | [[Paper](https://dl.acm.org/doi/10.1145/3442381.3449875)] [[Code](https://github.com/AnryYang/ACMin)] |

|Scalable Community Detection via Parallel Correlation Clustering | VLDB | 2021 | Shi _et al._ | [[Paper](http://vldb.org/pvldb/vol14/p2305-shi.pdf)] [[Code](https://github.com/jeshi96/parallel-correlation-clustering)] |

|Proximity-based group formation game model for community detection in social network | Knowl.-Based Syst. | 2021 | PBCD | [[Paper](https://linkinghub.elsevier.com/retrieve/pii/S0950705120307991)] |

|When random initializations help: A study of variational inference for community detection | J. Mach. Learn. Res. | 2021 | BCAVI | [[Paper](https://www.jmlr.org/papers/volume22/19-630/19-630.pdf)] |

|Compactness preserving community computation via a network generative process | IEEE TETCI | 2021 | FCOCD | [[Paper](https://ieeexplore.ieee.org/document/9548676)] |

|Identification of communities with multi-semantics via bayesian generative model | IEEE TBD | 2021 | ICMS | [[Paper](https://ieeexplore.ieee.org/document/9632396)] |

|A network embedding-enhanced Bayesian model for generalized community detection in complex networks | Inf. Sci. | 2021 | NEGCD | [[Paper](https://doi.org/10.1016/j.ins.2021.06.020)] |

|Multi-objective evolutionary clustering for large-scale dynamic community detection | Inf. Sci. | 2021 |  DYN-MODPSO  | [[Paper](https://www.sciencedirect.com/science/article/pii/S0020025520311117)] |

|A joint community detection model: Integrating directed and undirected probabilistic graphical models via factor graph with attention mechanism | IEEE TBD | 2021 | AdaMRF | [[Paper](https://ieeexplore.ieee.org/document/9511816)] |

|SimClusters: Community-based representations for heterogeneous recommendations at Twitter | KDD | 2020 | SimClusters | [[Paper](https://doi.org/10.1145/3394486.3403370)] [[Code](https://github.com/twitter/sbf)] | 

|Evolutionary markov dynamics for network community detection | IEEE TKDE | 2020 | ePMCL | [[Paper](https://ieeexplore.ieee.org/document/9099469)] |

|A network reduction-based multiobjective evolutionary algorithm for community detection in large-scale complex networks | IEEE TCYB | 2020 | RMOEA | [[Paper](https://ieeexplore.ieee.org/document/8486719)] |

|Integrating group homophily and individual personality of topics can better model network communities | ICDM | 2020 | GHIPT | [[Paper](https://ieeexplore.ieee.org/document/9338379)] |

|Community preserving network embedding based on memetic algorithm | IEEE TETCI | 2020 | MemeRep | [[Paper](https://ieeexplore.ieee.org/document/8449095)] |

|Detecting the evolving community structure in dynamic social networks | World Wide Web J. | 2020 | DECS | [[Paper](https://link.springer.com/article/10.1007/s11280-019-00710-z)] [[Code](https://github.com/FanzhenLiu/DECS)] |

|EdMot: An edge enhancement approach for motif-aware community detection | KDD | 2019 | EdMot | [[Paper](https://dl.acm.org/doi/10.1145/3292500.3330882)] |

|LPANNI: Overlapping community detection using label propagation in large-scale complex networks | IEEE TKDE | 2019 | LPANNI | [[Paper](https://ieeexplore.ieee.org/document/8443129)] |

|Detecting prosumer-community groups in smart grids from the multiagent perspective | IEEE TSMC | 2019 | PVMAS | [[Paper](https://ieeexplore.ieee.org/document/8660684)] |

|Local community mining on distributed and dynamic networks from a multiagent perspective | IEEE TCYB | 2016 | AOCCM | [[Paper](https://ieeexplore.ieee.org/document/7124425)] |

|General optimization technique for high-quality community detection in complex networks | Phys. Rev. E | 2014 | Combo | [[Paper](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.90.012811)] |

|Spectral methods for community detection and graph partitioning | Phys. Rev. E | 2013 | -- | [[Paper](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.88.042822)] |

|Stochastic blockmodels and community structure in networks | Phys. Rev. E | 2011 | DCSBM | [[Paper](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.83.016107)] |

----------

## Datasets

### Citation/Co-authorship Networks

- Citeseer, Cora, Pubmed https://linqs.soe.ucsc.edu/data

- DBLP http://snap.stanford.edu/data/com-DBLP.html

- Chemistry, Computer Science, Medicine, Engineering http://kddcup2016.azurewebsites.net/

### Online Social Networks

- Facebook http://snap.stanford.edu/data/ego-Facebook.html

- Epinions http://www.epinions.com/

- Youtube http://snap.stanford.edu/data/com-Youtube.html

- Last.fm https://www.last.fm/

- LiveJournal http://snap.stanford.edu/data/soc-LiveJournal1.html

- Gplus http://snap.stanford.edu/data/ego-Gplus.html

### Traditional Social Networks

- Cellphone Calls http://www.cs.umd.edu/hcil/VASTchallenge08/

- Enron Mail http://www.cs.cmu.edu/~enron/

- Friendship https://dl.acm.org/doi/10.1145/2501654.2501657

- Rados http://networkrepository.com/ia-radoslaw-email.php 

- Karate, Football, Dolphin http://www-personal.umich.edu/~mejn/netdata/

### Webpage Networks

- IMDb https://www.imdb.com/

- Wiki https://linqs.soe.ucsc.edu/data

### Product Co-purchasing Networks

- Amazon http://snap.stanford.edu/data/#amazon

### Other Networks

- Internet http://www-personal.umich.edu/~mejn/netdata/

- Java https://github.com/gephi/gephi/wiki/Datasets

- Hypertext http://www.sociopatterns.org/datasets

 

 ----------

## Tools

- Gephi https://gephi.org/

- Pajek http://mrvar.fdv.uni-lj.si/pajek/

- LFR https://www.santofortunato.net/resources

----------

**Disclaimer**

If you have any questions, please feel free to contact us.

Emails: [email protected], [email protected]