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Fix/minor changes #106

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Fix/minor changes #106

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9324d7e
Updated setup.py contents
jernsting Oct 20, 2022
fc3025b
Updated README contents
jernsting Oct 20, 2022
0ef87e7
Updated and removed status badges
jernsting Oct 20, 2022
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6 changes: 2 additions & 4 deletions README.md
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[![Python application](https://github.com/jernsting/GEM/actions/workflows/lint_and_test.yml/badge.svg)](https://github.com/jernsting/GEM/actions/workflows/lint_and_test.yml)
[![Coverage Status](https://coveralls.io/repos/github/jernsting/GEM/badge.svg?branch=master)](https://coveralls.io/github/jernsting/GEM?branch=master)
[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=jernsting_GEM&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=jernsting_GEM)
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I you'd like Coverage and Quality Gate Status badges, you need to add the project to https://coveralls.io and https://sonarcloud.io/ (free for open source projects).

For coveralls you also need to setup a repository secret.

[![Python application](https://github.com/palash1992/GEM/actions/workflows/lint_and_test.yml/badge.svg)](https://github.com/palash1992/GEM/actions/workflows/lint_and_test.yml)

# GEM: Graph Embedding Methods
Many physical systems in the world involve interactions between different entities and can be represented as graphs. Understanding the structure and analyzing properties of graphs are hence paramount to developing insights into the physical systems. Graph embedding, which aims to represent a graph in a low dimensional vector space, takes a step in this direction. The embeddings can be used for various tasks on graphs such as visualization, clustering, classification and prediction.
Expand Down Expand Up @@ -59,7 +57,7 @@ sudo python setup.py install

Or installing via pipwith git:
```bash
pip install git+https://github.com/jernsting/nxt_gem.git
pip install git+https://github.com/palash1992/GEM.git
```

## Usage
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15 changes: 7 additions & 8 deletions setup.py
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from setuptools import setup, find_packages



VERSION = '2.0.1'
DISTNAME = 'nxt_gem'
MAINTAINER = 'Jan Ernsting'
MAINTAINER_EMAIL = 'j.ernsting@uni-muenster.de'
DESCRIPTION = 'nxt_gem: A Python module for Graph Embedding Methods'
VERSION = '1.0.0'
DISTNAME = 'gem'
MAINTAINER = 'Palash Goyal'
MAINTAINER_EMAIL = 'palashgo@usc.edu'
DESCRIPTION = 'GEM: A Python module for Graph Embedding Methods'
LONG_DESCRIPTION = open('README.md').read()
URL = 'https://github.com/jernsting/nxt_gem'
DOWNLOAD_URL = 'https://github.com/jernsting/nxt_gem/archive/' + VERSION + '.tar.gz'
URL = 'https://github.com/palash1992/GEM'
DOWNLOAD_URL = 'https://github.com/palash1992/GEM'
KEYWORDS = ['graph embedding', 'network analysis',
'network embedding', 'data mining', 'machine learning']
LICENSE = 'BSD'
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