PyGOD: A Python Library for Graph Outlier Detection
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2022
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910466040659968 |
|---|---|
| author | Liu, Kay Dou, Yingtong Ding, Xueying Hu, Xiyang Zhang, Ruitong Peng, Hao Sun, Lichao Yu, Philip S. |
| author_facet | Liu, Kay Dou, Yingtong Ding, Xueying Hu, Xiyang Zhang, Ruitong Peng, Hao Sun, Lichao Yu, Philip S. |
| contents | PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for outlier detection under an easy-to-use, well-documented API designed for use by both researchers and practitioners. PyGOD provides modularized components of the different detectors implemented so that users can easily customize each detector for their purposes. To ease the construction of detection workflows, PyGOD offers numerous commonly used utility functions. To scale computation to large graphs, PyGOD supports functionalities for deep models such as sampling and mini-batch processing. PyGOD uses best practices in fostering code reliability and maintainability, including unit testing, continuous integration, and code coverage. To facilitate accessibility, PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_12095 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | PyGOD: A Python Library for Graph Outlier Detection Liu, Kay Dou, Yingtong Ding, Xueying Hu, Xiyang Zhang, Ruitong Peng, Hao Sun, Lichao Yu, Philip S. Machine Learning Social and Information Networks PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for outlier detection under an easy-to-use, well-documented API designed for use by both researchers and practitioners. PyGOD provides modularized components of the different detectors implemented so that users can easily customize each detector for their purposes. To ease the construction of detection workflows, PyGOD offers numerous commonly used utility functions. To scale computation to large graphs, PyGOD supports functionalities for deep models such as sampling and mini-batch processing. PyGOD uses best practices in fostering code reliability and maintainability, including unit testing, continuous integration, and code coverage. To facilitate accessibility, PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI). |
| title | PyGOD: A Python Library for Graph Outlier Detection |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2204.12095 |