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0.2 inductiveGRL-0.2-py3-none-any.whl

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Project: inductiveGRL
Version: 0.2
Filename: inductiveGRL-0.2-py3-none-any.whl
Download: [link]
Size: 6551
MD5: aad11e8b93009e9c5b20222613edb177
SHA256: 7e910bfb4c3059e0fd9bf8da58a1533b61cb6b8eb833bc3929dc0ec11fd03372
Uploaded: 2020-08-05 11:28:26 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: inductiveGRL
Version: 0.2
Summary: the code used in the experimental setup of our paper 'Inductive Graph Representation Learning for Fraud Detection'.
Author: Charles Van Damme
Author-Email: charles.vandamme[at]student.kuleuven.be
Home-Page: https://github.com/Charlesvandamme/Inductive-Graph-Representation-Learning-for-Fraud-Detection
Download-Url: https://github.com/rafaelvanbelle/Inductive-Graph-Representation-Learning-for-Fraud-Detection/releases/download/v0.2/inductiveGRL-0.2.tar.gz
License: MIT
Requires-Dist: ipynb
Requires-Dist: nbimporter
Requires-Dist: stellargraph
Requires-Dist: networkx
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-plot
Requires-Dist: matplotlib
Requires-Dist: sklearn
Requires-Dist: xgboost
Requires-Dist: keras
Requires-Dist: tensorflow
Requires-Dist: matlab-kernel
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WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.34.2)
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
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inductiveGRL/evaluation.py sha256=KENBWiUpSk3BKC5cOKkSVHVr9_k0r4wtbL0w8a422ow 3120
inductiveGRL/graphconstruction.py sha256=J2qydC4w4DgElZLdLub77ffm-Ljnj4Z8D9wXLqpOGos 1811
inductiveGRL/hinsage.py sha256=MMU53wP8MFUUC_kUAYl9BUx41ES1VfFedtiti_mscC4 5124
inductiveGRL/timeframes.py sha256=qjdFCNKBMKfEFIQDookkQV-PuwiIprmnF-nO2X9x94g 3734
inductiveGRL-0.2.dist-info/METADATA sha256=AIub6cHpqR3cZ5JROilhQpCa5myeXj_p-FdVnM8cTAQ 876
inductiveGRL-0.2.dist-info/WHEEL sha256=g4nMs7d-Xl9-xC9XovUrsDHGXt-FT0E17Yqo92DEfvY 92
inductiveGRL-0.2.dist-info/top_level.txt sha256=nivWWTQMaKHGAFHATo1_D4Sux6dj7tAvCAkOFP5hbnA 13
inductiveGRL-0.2.dist-info/RECORD — —

top_level.txt

inductiveGRL