version 1.0

update

Author: wenchieh

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

AUTHOR

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+SpecGreedy: Unified Dense Subgraph Detection
+Copyright (C) Wenchieh Feng

Makefile

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+#
+# Makefile for SPECGREEDY
+#
+
+all: clean demo
+
+clean:
+	rm -rf outs
+	mkdir outs
+	find ./src -name *.pyc | xargs rm -rf
+
+demo:
+	@echo "run demo."
+	./demo.sh
+    @echo
+	
+tar:
+	rm -rf output/*
+	@echo [PROJECT]
+	./package.sh
+	@echo [PROJECT] "done."
+	@echo

README.md

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-# specgreedy
+SpecGreedy: Unified Dense Subgraph Detection
+==================
+**SpecGreedy** is a unified fast algorithm for the generalized densest subgraph detection problem (GenDS)
+based on the graph spectral properties and a greedy peeling approach. 
+
+- _Theory & Correspondences_: the unified formulation, GenDS, subsumes many real problems from different applications; 
+                            and its optimization is guaranteed by the spectral theory.
+- _Scalable_: SpecGreedy runs linearly with the graph size.
+- _Effectiveness_: The performance (solution-quality and speedy) of SpecGreedy is verified on **40** real world networks; 
+                 and it can find some interesting patterns in real applications, 
+                 like the sudden bursts in research co-authorship relationships    
+
+
+**[The repo will be updated soon]**
+
+Datasets
+========================
+
+The datasets used are available online, they are from some popular network repositories, including 
+[Stanford's SNAP](http://snap.stanford.edu/data), 
+[AUS's Social Computing Data Repository](http://socialcomputing.asu.edu/), 
+[Network Repository](http://networkrepository.com/),
+[Aminer scholar datasets](https://www.aminer.cn/data),
+[Koblenz Nwtwork Collection](http://konect.uni-koblenz.de/networks/), and
+[MPI-SWS social datasets](http://socialnetworks.mpi-sws.org).
+  
+
+Environment
+=======================
+Python 3.6 is supported in the current version.
+
+To install required libraries, please type
+```bash
+pip install -r requirements
+```
+----
+
+
+Running Demo
+========================
+
+Demo for detecting the densest subgraph, please type
+```bash
+make
+```
+
+
+Reference
+========================
+If you use this code as part of any published research, please acknowledge the following papers.
+```
+@inproceedings{feng2020specgreedy,
+  title={SpecGreedy: Unified Dense Subgraph Detection},
+  author={Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, and Xueqi Cheng},
+  booktitle={European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
+  year={2020},
+}
+```