.. image:: https://raw.githubusercontent.com/pygod-team/pygod/main/docs/pygod_logo.png
:width: 1050
:alt: PyGOD Logo
:align: center
.. image:: https://img.shields.io/pypi/v/pygod.svg?color=brightgreen
:target: https://pypi.org/project/pygod/
:alt: PyPI version
.. image:: https://readthedocs.org/projects/pygod/badge/?version=latest
:target: https://docs.pygod.org/en/latest/?badge=latest
:alt: Documentation status
.. image:: https://img.shields.io/github/stars/pygod-team/pygod.svg
:target: https://github.com/pygod-team/pygod/stargazers
:alt: GitHub stars
.. image:: https://img.shields.io/github/forks/pygod-team/pygod.svg?color=blue
:target: https://github.com/pygod-team/pygod/network
:alt: GitHub forks
.. image:: https://static.pepy.tech/personalized-badge/pygod?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads
:target: https://pepy.tech/project/pygod
:alt: PyPI downloads
.. image:: https://github.com/pygod-team/pygod/actions/workflows/testing.yml/badge.svg
:target: https://github.com/pygod-team/pygod/actions/workflows/testing.yml
:alt: testing
.. image:: https://coveralls.io/repos/github/pygod-team/pygod/badge.svg?branch=main
:target: https://coveralls.io/github/pygod-team/pygod?branch=main
:alt: Coverage Status
.. image:: https://img.shields.io/github/license/pygod-team/pygod.svg
:target: https://github.com/pygod-team/pygod/blob/master/LICENSE
:alt: License
-----
PyGOD is a **Python library** for **graph outlier detection** (anomaly detection).
This exciting yet challenging field has many key applications, e.g., detecting
suspicious activities in social networks [#Dou2020Enhancing]_ and security systems [#Cai2021Structural]_.
PyGOD includes **10+** graph outlier detection algorithms.
For consistency and accessibility, PyGOD is developed on top of `PyTorch Geometric (PyG) <https://www.pyg.org/>`_
and `PyTorch <https://pytorch.org/>`_, and follows the API design of `PyOD <https://github.com/yzhao062/pyod>`_.
See examples below for detecting outliers with PyGOD in 5 lines!
**PyGOD is featured for**:
* **Unified APIs, detailed documentation, and interactive examples** across various graph-based algorithms.
* **Comprehensive coverage** of 10+ graph outlier detectors.
* **Full support of detections at multiple levels**, such as node-, edge-, and graph-level tasks.
* **Scalable design for processing large graphs** via mini-batch and sampling.
* **Streamline data processing with PyG**--fully compatible with PyG data objects.
**Outlier Detection Using PyGOD with 5 Lines of Code**\ :
.. code-block:: python
# train a dominant detector
from pygod.detector import DOMINANT
model = DOMINANT(num_layers=4, epoch=20) # hyperparameters can be set here
model.fit(train_data) # input data is a PyG data object
# get outlier scores on the training data (transductive setting)
score = model.decision_score_
# predict labels and scores on the testing data (inductive setting)
pred, score = model.predict(test_data, return_score=True)
**Citing PyGOD**\ :
Our `software paper <https://arxiv.org/abs/2204.12095>`_ and `benchmark paper <https://proceedings.neurips.cc/paper_files/paper/2022/hash/acc1ec4a9c780006c9aafd595104816b-Abstract-Datasets_and_Benchmarks.html>`_ are publicly available.
If you use PyGOD or BOND in a scientific publication, we would appreciate citations to the following papers::
@article{liu2022pygod,
title={PyGOD: A Python Library for Graph Outlier Detection},
author={Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Chen, George H. and Jia, Zhihao and Yu, Philip S.},
journal={arXiv preprint arXiv:2204.12095},
year={2022}
}
@article{liu2022bond,
title={Bond: Benchmarking unsupervised outlier node detection on static attributed graphs},
author={Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Sun, Lichao and Li, Jundong and Chen, George H. and Jia, Zhihao and Yu, Philip S.},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={27021--27035},
year={2022}
}
or::
Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K. and Chen, G.H., Jia, Z., and Yu, P.S. 2022. PyGOD: A Python Library for Graph Outlier Detection. arXiv preprint arXiv:2204.12095.
Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K. and Sun, L., Li, J., Chen, G.H., Jia, Z., and Yu, P.S. 2022. Bond: Benchmarking unsupervised outlier node detection on static attributed graphs. Advances in Neural Information Processing Systems, 35, pp.27021-27035.
----
Installation
^^^^^^^^^^^^
**Note on PyG and PyTorch Installation**\ :
PyGOD depends on `torch <https://https://pytorch.org/get-started/locally/>`_ and `torch_geometric (including its optional dependencies) <https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html#>`_.
To streamline the installation, PyGOD does **NOT** install these libraries for you.
Please install them from the above links for running PyGOD:
* torch>=2.0.0
* torch_geometric>=2.3.0
It is recommended to use **pip** for installation.
Please make sure **the latest version** is installed, as PyGOD is updated frequently:
.. code-block:: bash
pip install pygod # normal install
pip install --upgrade pygod # or update if needed
Alternatively, you could clone and run setup.py file:
.. code-block:: bash
git clone https://github.com/pygod-team/pygod.git
cd pygod
pip install .
**Required Dependencies**\ :
* python>=3.8
* numpy>=1.24.3
* scikit-learn>=1.2.2
* scipy>=1.10.1
* networkx>=3.1
----
Quick Start for Outlier Detection with PyGOD
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
`"A Blitz Introduction" <https://docs.pygod.org/en/latest/tutorials/1_intro.html#sphx-glr-tutorials-1-intro-py>`_
demonstrates the basic API of PyGOD using the DOMINANT detector. **It is noted that the API across all other algorithms are consistent/similar**.
----
API Cheatsheet & Reference
^^^^^^^^^^^^^^^^^^^^^^^^^^
Full API Reference: (https://docs.pygod.org). API cheatsheet for all detectors:
* **fit(data)**\ : Fit the detector with train data.
* **predict(data)**\ : Predict on test data (train data if not provided) using the fitted detector.
Key Attributes of a fitted detector:
* **decision_score_**\ : The outlier scores of the input data. Outliers tend to have higher scores.
* **label_**\ : The binary labels of the input data. 0 stands for inliers and 1 for outliers.
* **threshold_**\ : The determined threshold for binary classification. Scores above the threshold are outliers.
**Input of PyGOD**: Please pass in a `PyG Data object <https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Data.html#torch_geometric.data.Data>`_.
See `PyG data processing examples <https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html#data-handling-of-graphs>`_.
Implemented Algorithms
^^^^^^^^^^^^^^^^^^^^^^
================== ===== =========== =========== ========================================
Abbr Year Backbone Sampling Ref
================== ===== =========== =========== ========================================
SCAN 2007 Clustering No [#Xu2007Scan]_
GAE 2016 GNN+AE Yes [#Kipf2016Variational]_
Radar 2017 MF No [#Li2017Radar]_
ANOMALOUS 2018 MF No [#Peng2018Anomalous]_
ONE 2019 MF No [#Bandyopadhyay2019Outlier]_
DOMINANT 2019 GNN+AE Yes [#Ding2019Deep]_
DONE 2020 MLP+AE Yes [#Bandyopadhyay2020Outlier]_
AdONE 2020 MLP+AE Yes [#Bandyopadhyay2020Outlier]_
AnomalyDAE 2020 GNN+AE Yes [#Fan2020AnomalyDAE]_
GAAN 2020 GAN Yes [#Chen2020Generative]_
DMGD 2020 GNN+AE Yes [#Bandyopadhyay2020Integrating]_
OCGNN 2021 GNN Yes [#Wang2021One]_
CoLA 2021 GNN+AE+SSL Yes [#Liu2021Anomaly]_
GUIDE 2021 GNN+AE Yes [#Yuan2021Higher]_
CONAD 2022 GNN+AE+SSL Yes [#Xu2022Contrastive]_
GADNR 2024 GNN+AE Yes [#Roy2024Gadnr]_
================== ===== =========== =========== ========================================
----
How to Contribute
^^^^^^^^^^^^^^^^^
You are welcome to contribute to this exciting project:
See `contribution guide <https://github.com/pygod-team/pygod/blob/main/CONTRIBUTING.rst>`_ for more information.
----
PyGOD Team
^^^^^^^^^^
PyGOD is a great team effort by researchers from UIC, IIT, BUAA, ASU, and CMU.
Our core team members include:
`Kay Liu (UIC) <https://kayzliu.com/>`_,
`Yingtong Dou (UIC) <http://ytongdou.com/>`_,
`Yue Zhao (CMU) <https://www.andrew.cmu.edu/user/yuezhao2/>`_,
`Xueying Ding (CMU) <https://scholar.google.com/citations?user=U9CMsh0AAAAJ&hl=en>`_,
`Xiyang Hu (CMU) <https://www.andrew.cmu.edu/user/xiyanghu/>`_,
`Ruitong Zhang (BUAA) <https://github.com/pygod-team/pygod>`_,
`Kaize Ding (ASU) <https://www.public.asu.edu/~kding9/>`_,
`Canyu Chen (IIT) <https://github.com/pygod-team/pygod>`_,
Reach out us by submitting an issue report or send an email to dev@pygod.org.
----
Reference
^^^^^^^^^
.. [#Dou2020Enhancing] Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H. and Yu, P.S., 2020, October. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM).
.. [#Cai2021Structural] Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D. and Chen, H., 2021, October. Structural temporal graph neural networks for anomaly detection in dynamic graphs. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM).
.. [#Xu2007Scan] Xu, X., Yuruk, N., Feng, Z. and Schweiger, T.A., 2007, August. Scan: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
.. [#Kipf2016Variational] Kipf, T.N. and Welling, M., 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308.
.. [#Li2017Radar] Li, J., Dani, H., Hu, X. and Liu, H., 2017, August. Radar: Residual Analysis for Anomaly Detection in Attributed Networks. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI).
.. [#Peng2018Anomalous] Peng, Z., Luo, M., Li, J., Liu, H. and Zheng, Q., 2018, July. ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI).
.. [#Bandyopadhyay2019Outlier] Bandyopadhyay, S., Lokesh, N. and Murty, M.N., 2019, July. Outlier aware network embedding for attributed networks. In Proceedings of the AAAI conference on artificial intelligence (AAAI).
.. [#Ding2019Deep] Ding, K., Li, J., Bhanushali, R. and Liu, H., 2019, May. Deep anomaly detection on attributed networks. In Proceedings of the SIAM International Conference on Data Mining (SDM).
.. [#Bandyopadhyay2020Outlier] Bandyopadhyay, S., Vivek, S.V. and Murty, M.N., 2020, January. Outlier resistant unsupervised deep architectures for attributed network embedding. In Proceedings of the International Conference on Web Search and Data Mining (WSDM).
.. [#Fan2020AnomalyDAE] Fan, H., Zhang, F. and Li, Z., 2020, May. AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
.. [#Chen2020Generative] Chen, Z., Liu, B., Wang, M., Dai, P., Lv, J. and Bo, L., 2020, October. Generative adversarial attributed network anomaly detection. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM).
.. [#Bandyopadhyay2020Integrating] Bandyopadhyay, S., Vishal Vivek, S. and Murty, M.N., 2020. Integrating network embedding and community outlier detection via multiclass graph description. Frontiers in Artificial Intelligence and Applications, (FAIA).
.. [#Wang2021One] Wang, X., Jin, B., Du, Y., Cui, P., Tan, Y. and Yang, Y., 2021. One-class graph neural networks for anomaly detection in attributed networks. Neural computing and applications.
.. [#Liu2021Anomaly] Liu, Y., Li, Z., Pan, S., Gong, C., Zhou, C. and Karypis, G., 2021. Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE transactions on neural networks and learning systems (TNNLS).
.. [#Yuan2021Higher] Yuan, X., Zhou, N., Yu, S., Huang, H., Chen, Z. and Xia, F., 2021, December. Higher-order Structure Based Anomaly Detection on Attributed Networks. In 2021 IEEE International Conference on Big Data (Big Data).
.. [#Xu2022Contrastive] Xu, Z., Huang, X., Zhao, Y., Dong, Y., and Li, J., 2022. Contrastive Attributed Network Anomaly Detection with Data Augmentation. In Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
.. [#Roy2024Gadnr] Roy, A., Shu, J., Li, J., Yang, C., Elshocht, O., Smeets, J. and Li, P., 2024. GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM).
Raw data
{
"_id": null,
"home_page": "https://github.com/pygod-team/pygod/",
"name": "pygod",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "outlier detection,anomaly detection,graph mining,data mining,neural networks,graph neural networks",
"author": "PyGOD Team",
"author_email": "dev@pygod.org",
"download_url": "https://files.pythonhosted.org/packages/4a/f5/0a9822e6a7931292e7738b3027e6dbab0e0f37f9364c4e28b11c05c1800b/pygod-1.1.0.tar.gz",
"platform": null,
"description": ".. image:: https://raw.githubusercontent.com/pygod-team/pygod/main/docs/pygod_logo.png\n :width: 1050\n :alt: PyGOD Logo\n :align: center\n\n.. image:: https://img.shields.io/pypi/v/pygod.svg?color=brightgreen\n :target: https://pypi.org/project/pygod/\n :alt: PyPI version\n\n.. image:: https://readthedocs.org/projects/pygod/badge/?version=latest\n :target: https://docs.pygod.org/en/latest/?badge=latest\n :alt: Documentation status\n\n.. image:: https://img.shields.io/github/stars/pygod-team/pygod.svg\n :target: https://github.com/pygod-team/pygod/stargazers\n :alt: GitHub stars\n\n.. image:: https://img.shields.io/github/forks/pygod-team/pygod.svg?color=blue\n :target: https://github.com/pygod-team/pygod/network\n :alt: GitHub forks\n\n.. image:: https://static.pepy.tech/personalized-badge/pygod?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads\n :target: https://pepy.tech/project/pygod\n :alt: PyPI downloads\n\n.. image:: https://github.com/pygod-team/pygod/actions/workflows/testing.yml/badge.svg\n :target: https://github.com/pygod-team/pygod/actions/workflows/testing.yml\n :alt: testing\n\n.. image:: https://coveralls.io/repos/github/pygod-team/pygod/badge.svg?branch=main\n :target: https://coveralls.io/github/pygod-team/pygod?branch=main\n :alt: Coverage Status\n\n.. image:: https://img.shields.io/github/license/pygod-team/pygod.svg\n :target: https://github.com/pygod-team/pygod/blob/master/LICENSE\n :alt: License\n\n\n-----\n\nPyGOD is a **Python library** for **graph outlier detection** (anomaly detection).\nThis exciting yet challenging field has many key applications, e.g., detecting\nsuspicious activities in social networks [#Dou2020Enhancing]_ and security systems [#Cai2021Structural]_.\n\nPyGOD includes **10+** graph outlier detection algorithms.\nFor consistency and accessibility, PyGOD is developed on top of `PyTorch Geometric (PyG) <https://www.pyg.org/>`_\nand `PyTorch <https://pytorch.org/>`_, and follows the API design of `PyOD <https://github.com/yzhao062/pyod>`_.\nSee examples below for detecting outliers with PyGOD in 5 lines!\n\n\n**PyGOD is featured for**:\n\n* **Unified APIs, detailed documentation, and interactive examples** across various graph-based algorithms.\n* **Comprehensive coverage** of 10+ graph outlier detectors.\n* **Full support of detections at multiple levels**, such as node-, edge-, and graph-level tasks.\n* **Scalable design for processing large graphs** via mini-batch and sampling.\n* **Streamline data processing with PyG**--fully compatible with PyG data objects.\n\n**Outlier Detection Using PyGOD with 5 Lines of Code**\\ :\n\n.. code-block:: python\n\n\n # train a dominant detector\n from pygod.detector import DOMINANT\n\n model = DOMINANT(num_layers=4, epoch=20) # hyperparameters can be set here\n model.fit(train_data) # input data is a PyG data object\n\n # get outlier scores on the training data (transductive setting)\n score = model.decision_score_\n\n # predict labels and scores on the testing data (inductive setting)\n pred, score = model.predict(test_data, return_score=True)\n\n\n**Citing PyGOD**\\ :\n\nOur `software paper <https://arxiv.org/abs/2204.12095>`_ and `benchmark paper <https://proceedings.neurips.cc/paper_files/paper/2022/hash/acc1ec4a9c780006c9aafd595104816b-Abstract-Datasets_and_Benchmarks.html>`_ are publicly available.\nIf you use PyGOD or BOND in a scientific publication, we would appreciate citations to the following papers::\n\n @article{liu2022pygod,\n title={PyGOD: A Python Library for Graph Outlier Detection},\n author={Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Chen, George H. and Jia, Zhihao and Yu, Philip S.},\n journal={arXiv preprint arXiv:2204.12095},\n year={2022}\n }\n @article{liu2022bond,\n title={Bond: Benchmarking unsupervised outlier node detection on static attributed graphs},\n author={Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Sun, Lichao and Li, Jundong and Chen, George H. and Jia, Zhihao and Yu, Philip S.},\n journal={Advances in Neural Information Processing Systems},\n volume={35},\n pages={27021--27035},\n year={2022}\n }\n\nor::\n\n Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K. and Chen, G.H., Jia, Z., and Yu, P.S. 2022. PyGOD: A Python Library for Graph Outlier Detection. arXiv preprint arXiv:2204.12095.\n Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K. and Sun, L., Li, J., Chen, G.H., Jia, Z., and Yu, P.S. 2022. Bond: Benchmarking unsupervised outlier node detection on static attributed graphs. Advances in Neural Information Processing Systems, 35, pp.27021-27035.\n\n----\n\nInstallation\n^^^^^^^^^^^^\n\n**Note on PyG and PyTorch Installation**\\ :\nPyGOD depends on `torch <https://https://pytorch.org/get-started/locally/>`_ and `torch_geometric (including its optional dependencies) <https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html#>`_.\nTo streamline the installation, PyGOD does **NOT** install these libraries for you.\nPlease install them from the above links for running PyGOD:\n\n* torch>=2.0.0\n* torch_geometric>=2.3.0\n\nIt is recommended to use **pip** for installation.\nPlease make sure **the latest version** is installed, as PyGOD is updated frequently:\n\n.. code-block:: bash\n\n pip install pygod # normal install\n pip install --upgrade pygod # or update if needed\n\nAlternatively, you could clone and run setup.py file:\n\n.. code-block:: bash\n\n git clone https://github.com/pygod-team/pygod.git\n cd pygod\n pip install .\n\n**Required Dependencies**\\ :\n\n* python>=3.8\n* numpy>=1.24.3\n* scikit-learn>=1.2.2\n* scipy>=1.10.1\n* networkx>=3.1\n\n\n----\n\n\nQuick Start for Outlier Detection with PyGOD\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n`\"A Blitz Introduction\" <https://docs.pygod.org/en/latest/tutorials/1_intro.html#sphx-glr-tutorials-1-intro-py>`_\ndemonstrates the basic API of PyGOD using the DOMINANT detector. **It is noted that the API across all other algorithms are consistent/similar**.\n\n\n----\n\n\nAPI Cheatsheet & Reference\n^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nFull API Reference: (https://docs.pygod.org). API cheatsheet for all detectors:\n\n* **fit(data)**\\ : Fit the detector with train data.\n* **predict(data)**\\ : Predict on test data (train data if not provided) using the fitted detector.\n\nKey Attributes of a fitted detector:\n\n* **decision_score_**\\ : The outlier scores of the input data. Outliers tend to have higher scores.\n* **label_**\\ : The binary labels of the input data. 0 stands for inliers and 1 for outliers.\n* **threshold_**\\ : The determined threshold for binary classification. Scores above the threshold are outliers.\n\n**Input of PyGOD**: Please pass in a `PyG Data object <https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Data.html#torch_geometric.data.Data>`_.\nSee `PyG data processing examples <https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html#data-handling-of-graphs>`_.\n\n\nImplemented Algorithms\n^^^^^^^^^^^^^^^^^^^^^^\n\n================== ===== =========== =========== ========================================\nAbbr Year Backbone Sampling Ref\n================== ===== =========== =========== ========================================\nSCAN 2007 Clustering No [#Xu2007Scan]_\nGAE 2016 GNN+AE Yes [#Kipf2016Variational]_\nRadar 2017 MF No [#Li2017Radar]_\nANOMALOUS 2018 MF No [#Peng2018Anomalous]_\nONE 2019 MF No [#Bandyopadhyay2019Outlier]_\nDOMINANT 2019 GNN+AE Yes [#Ding2019Deep]_\nDONE 2020 MLP+AE Yes [#Bandyopadhyay2020Outlier]_\nAdONE 2020 MLP+AE Yes [#Bandyopadhyay2020Outlier]_\nAnomalyDAE 2020 GNN+AE Yes [#Fan2020AnomalyDAE]_\nGAAN 2020 GAN Yes [#Chen2020Generative]_\nDMGD 2020 GNN+AE Yes [#Bandyopadhyay2020Integrating]_\nOCGNN 2021 GNN Yes [#Wang2021One]_\nCoLA 2021 GNN+AE+SSL Yes [#Liu2021Anomaly]_\nGUIDE 2021 GNN+AE Yes [#Yuan2021Higher]_\nCONAD 2022 GNN+AE+SSL Yes [#Xu2022Contrastive]_\nGADNR 2024 GNN+AE Yes [#Roy2024Gadnr]_\n================== ===== =========== =========== ========================================\n\n\n----\n\nHow to Contribute\n^^^^^^^^^^^^^^^^^\n\nYou are welcome to contribute to this exciting project:\n\nSee `contribution guide <https://github.com/pygod-team/pygod/blob/main/CONTRIBUTING.rst>`_ for more information.\n\n\n----\n\nPyGOD Team\n^^^^^^^^^^\n\nPyGOD is a great team effort by researchers from UIC, IIT, BUAA, ASU, and CMU.\nOur core team members include:\n\n`Kay Liu (UIC) <https://kayzliu.com/>`_,\n`Yingtong Dou (UIC) <http://ytongdou.com/>`_,\n`Yue Zhao (CMU) <https://www.andrew.cmu.edu/user/yuezhao2/>`_,\n`Xueying Ding (CMU) <https://scholar.google.com/citations?user=U9CMsh0AAAAJ&hl=en>`_,\n`Xiyang Hu (CMU) <https://www.andrew.cmu.edu/user/xiyanghu/>`_,\n`Ruitong Zhang (BUAA) <https://github.com/pygod-team/pygod>`_,\n`Kaize Ding (ASU) <https://www.public.asu.edu/~kding9/>`_,\n`Canyu Chen (IIT) <https://github.com/pygod-team/pygod>`_,\n\nReach out us by submitting an issue report or send an email to dev@pygod.org.\n\n----\n\nReference\n^^^^^^^^^\n\n.. [#Dou2020Enhancing] Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H. and Yu, P.S., 2020, October. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM).\n\n.. [#Cai2021Structural] Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D. and Chen, H., 2021, October. Structural temporal graph neural networks for anomaly detection in dynamic graphs. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM).\n\n.. [#Xu2007Scan] Xu, X., Yuruk, N., Feng, Z. and Schweiger, T.A., 2007, August. Scan: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).\n\n.. [#Kipf2016Variational] Kipf, T.N. and Welling, M., 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308.\n\n.. [#Li2017Radar] Li, J., Dani, H., Hu, X. and Liu, H., 2017, August. Radar: Residual Analysis for Anomaly Detection in Attributed Networks. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI).\n\n.. [#Peng2018Anomalous] Peng, Z., Luo, M., Li, J., Liu, H. and Zheng, Q., 2018, July. ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI).\n\n.. [#Bandyopadhyay2019Outlier] Bandyopadhyay, S., Lokesh, N. and Murty, M.N., 2019, July. Outlier aware network embedding for attributed networks. In Proceedings of the AAAI conference on artificial intelligence (AAAI).\n\n.. [#Ding2019Deep] Ding, K., Li, J., Bhanushali, R. and Liu, H., 2019, May. Deep anomaly detection on attributed networks. In Proceedings of the SIAM International Conference on Data Mining (SDM).\n\n.. [#Bandyopadhyay2020Outlier] Bandyopadhyay, S., Vivek, S.V. and Murty, M.N., 2020, January. Outlier resistant unsupervised deep architectures for attributed network embedding. In Proceedings of the International Conference on Web Search and Data Mining (WSDM).\n\n.. [#Fan2020AnomalyDAE] Fan, H., Zhang, F. and Li, Z., 2020, May. AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).\n\n.. [#Chen2020Generative] Chen, Z., Liu, B., Wang, M., Dai, P., Lv, J. and Bo, L., 2020, October. Generative adversarial attributed network anomaly detection. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM).\n\n.. [#Bandyopadhyay2020Integrating] Bandyopadhyay, S., Vishal Vivek, S. and Murty, M.N., 2020. Integrating network embedding and community outlier detection via multiclass graph description. Frontiers in Artificial Intelligence and Applications, (FAIA).\n\n.. [#Wang2021One] Wang, X., Jin, B., Du, Y., Cui, P., Tan, Y. and Yang, Y., 2021. One-class graph neural networks for anomaly detection in attributed networks. Neural computing and applications.\n\n.. [#Liu2021Anomaly] Liu, Y., Li, Z., Pan, S., Gong, C., Zhou, C. and Karypis, G., 2021. Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE transactions on neural networks and learning systems (TNNLS).\n\n.. [#Yuan2021Higher] Yuan, X., Zhou, N., Yu, S., Huang, H., Chen, Z. and Xia, F., 2021, December. Higher-order Structure Based Anomaly Detection on Attributed Networks. In 2021 IEEE International Conference on Big Data (Big Data).\n\n.. [#Xu2022Contrastive] Xu, Z., Huang, X., Zhao, Y., Dong, Y., and Li, J., 2022. Contrastive Attributed Network Anomaly Detection with Data Augmentation. In Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).\n\n.. [#Roy2024Gadnr] Roy, A., Shu, J., Li, J., Yang, C., Elshocht, O., Smeets, J. and Li, P., 2024. GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM).\n",
"bugtrack_url": null,
"license": "BSD-2",
"summary": "A Python Library for Graph Outlier Detection (Anomaly Detection)",
"version": "1.1.0",
"project_urls": {
"Download": "https://github.com/pygod-team/pygod/archive/main.zip",
"Homepage": "https://github.com/pygod-team/pygod/"
},
"split_keywords": [
"outlier detection",
"anomaly detection",
"graph mining",
"data mining",
"neural networks",
"graph neural networks"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "61f71a7d1f5e621475fb70d6d284fc3c432df6702bf59d86e8c61fa7d14e02d7",
"md5": "0d0330bcb65294bbfe6236d886fcbc0a",
"sha256": "58404d0dfc2bcc32951b226247587432b94fc63779a46d53799fd46625f858a2"
},
"downloads": -1,
"filename": "pygod-1.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0d0330bcb65294bbfe6236d886fcbc0a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 86826,
"upload_time": "2024-02-04T21:25:16",
"upload_time_iso_8601": "2024-02-04T21:25:16.070461Z",
"url": "https://files.pythonhosted.org/packages/61/f7/1a7d1f5e621475fb70d6d284fc3c432df6702bf59d86e8c61fa7d14e02d7/pygod-1.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4af50a9822e6a7931292e7738b3027e6dbab0e0f37f9364c4e28b11c05c1800b",
"md5": "ec950de37ebcf09b652182bd68c63b29",
"sha256": "a9afd0db3a55b3ff20d24212cf85381db356456d6e21ba1f9687daf3619c5b36"
},
"downloads": -1,
"filename": "pygod-1.1.0.tar.gz",
"has_sig": false,
"md5_digest": "ec950de37ebcf09b652182bd68c63b29",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 55125,
"upload_time": "2024-02-04T21:25:17",
"upload_time_iso_8601": "2024-02-04T21:25:17.993938Z",
"url": "https://files.pythonhosted.org/packages/4a/f5/0a9822e6a7931292e7738b3027e6dbab0e0f37f9364c4e28b11c05c1800b/pygod-1.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-02-04 21:25:17",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "pygod-team",
"github_project": "pygod",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "pygod"
}
JSON
