Merge branch 'master' into dtdg

README.md

@@ -1,106 +1,125 @@
 <!-- # TGX -->
 ![TGX logo](imgs/2023_TGX_logo.png)
 
-# Temporal Graph Analysis with TGX (WSDM 2024 Demo Track)
+# Temporal Graph Analysis with TGX
 <h4>
 	<a href="https://arxiv.org/abs/2402.03651"><img src="https://img.shields.io/badge/arXiv-pdf-yellowgreen"></a>
 	<a href="https://complexdata-mila.github.io/TGX/"><img src="https://img.shields.io/badge/docs-orange"></a>
 </h4> 
 
-TGX supports all datasets from [TGB](https://tgb.complexdatalab.com/) and [Poursafaei et al. 2022](https://openreview.net/forum?id=1GVpwr2Tfdg) as well as any custom dataset in `.csv` format. 
-TGX provides numerous temporal graph visualization plots and statistics out of the box
+This repository contains the code for the paper "Temporal Graph Analysis with TGX" (WSDM 2024, Demo Track).
 
+TGX overview:
+- TGX supports all datasets from [TGB](https://tgb.complexdatalab.com/) and [Poursafaei et al. 2022](https://openreview.net/forum?id=1GVpwr2Tfdg) as well as any custom dataset in `.csv` format. 
+- TGX provides numerous temporal graph visualization plots and statistics out of the box.
 
-### Data Loading ###
-For detailed tutorial on how to load the datasets into `tgx.Graph`, see [`docs/tutorials/data_loader.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_loader.ipynb)
 
-1. Load TGB datasets
-```
-import tgx
-dataset = tgx.tgb_data("tgbl-wiki")
-ctdg = tgx.Graph(dataset)
-```
+## Dependecies
+TGX implementation works with `python >= 3.9` and can be installed as follows.
 
-2. Load built-in datasets
-```
-dataset = tgx.builtin.uci()
-ctdg = tgx.Graph(dataset)
-```
+1. Set up virtual environment (conda should work as well).
+	```
+	python -m venv ~/tgx_env/
+	source ~/tgx_env/bin/activate
+	```
 
-3. Load custom datasets from `.csv` 
-```
-from tgx.io.read import read_csv
-toy_fname = "docs/tutorials/toy_data.csv"
-edgelist = read_csv(toy_fname, header=True,index=False, t_col=0,)
-tgx.Graph(edgelist=edgelist)
-```
+2. Install external packages
+	```
+	pip install -r requirements.txt
+	```
 
-### Visualization and Statistics ###
-For detailed tutorial on how to generate visualizations and compute statistics for temporal graphs, see [`docs/tutorials/data_viz_stats.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_viz_stats.ipynb)
+3. Install local dependencies under root directory `/TGX`.
+	<!-- ```
+	pip install -e py-tgx
+	``` -->
+	```
+	pip install -e .
+	```
 
-1. Discretize the network (required for viz)
 
-```
-dataset = tgx.builtin.uci()
-ctdg = tgx.Graph(dataset)
-time_scale = "weekly"
-dtdg, ts_list = ctdg.discretize(time_scale=time_scale, store_unix=True)
-```
 
-2. Plot the number of nodes over time
+4. [alternatively] Install from `test-pypi`.
 
-```
-tgx.degree_over_time(dtdg, network_name="uci")
-```
+	```
+	pip install -i https://test.pypi.org/simple/ py-tgx
+	```
+	You can specify the version with `==`, note that the pypi version might not always be the most updated version
 
-3. Compute novelty index
-```
-tgx.get_novelty(dtdg)
-```
 
+5. [optional] Install `mkdocs` dependencies to serve the documentation locally.
+	```
+	pip install mkdocs-glightbox
+	```
 
-### Install dependency
-Our implementation works with python >= 3.9 and can be installed as follows
 
-1. set up virtual environment (conda should work as well)
-```
-python -m venv ~/tgx_env/
-source ~/tgx_env/bin/activate
-```
 
-2. install external packages
-```
-pip install -r requirements.txt
-```
+## Data Loading
+For detailed tutorial on how to load datasets as a `tgx.Graph`, see [`docs/tutorials/data_loader.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_loader.ipynb).
+Here are some simple examples on loading different datasets.
 
-3. install local dependencies under root directory `/TGX`
-<!-- ```
-pip install -e py-tgx
-``` -->
-```
-pip install -e .
-```
+1. Load TGB datasets.
+	```
+	import tgx
+	dataset = tgx.tgb_data("tgbl-wiki")
+	ctdg = tgx.Graph(dataset)
+	```
 
+2. Load built-in datasets.
+	```
+	dataset = tgx.builtin.uci()
+	ctdg = tgx.Graph(dataset)
+	```
 
+3. Load custom datasets from `.csv`.
+	```
+	from tgx.io.read import read_csv
+	toy_fname = "docs/tutorials/toy_data.csv"
+	edgelist = read_csv(toy_fname, header=True,index=False, t_col=0,)
+	tgx.Graph(edgelist=edgelist)
+	```
 
-3. [alternatively] install from test-pypi
+## Visualization and Statistics
+For detailed tutorial on how to generate visualizations and compute statistics for temporal graphs, see [`docs/tutorials/data_viz_stats.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_viz_stats.ipynb)
+Here are examples showing some of the TGX's functionalities.
 
-```
-pip install -i https://test.pypi.org/simple/ py-tgx
-```
-You can specify the version with `==`, note that the pypi version might not always be the most updated version
+1. **Discretize** the network (required for some visualization).
+	```
+	dataset = tgx.builtin.uci()
+	ctdg = tgx.Graph(dataset)
+	time_scale = "weekly"
+	dtdg, ts_list = ctdg.discretize(time_scale=time_scale, store_unix=True)
+	```
 
+2. Plot the **number of nodes over time**.
 
-4. [optional] install mkdocs dependencies to serve the documentation locally
-```
-pip install mkdocs-glightbox
-```
+	```
+	tgx.degree_over_time(dtdg, network_name="uci")
+	```
+
+3. Compute **novelty** index.
+	```
+	tgx.get_novelty(dtdg)
+	```
 
-### Creating new branch ###
+
+<!-- 
+### Creating new branch
 
 first create the branch on github
 ```
 git fetch origin
 
 git checkout -b test origin/test
-```
+``` -->
+
+
+### Citation
+If TGX is useful for your work, please consider citing it:
+```bibtex
+@article{shirzadkhani2024temporal,
+  title={Temporal Graph Analysis with TGX},
+  author={Shirzadkhani, Razieh and Huang, Shenyang and Kooshafar, Elahe and Rabbany, Reihaneh and Poursafaei, Farimah},
+  journal={arXiv preprint arXiv:2402.03651},
+  year={2024}
+}
+```

docs/tutorials/data_loader.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +297,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

requirements.txt

@@ -1,8 +1,64 @@
-matplotlib>=3.8.0
-pandas>=2.1.1
-numpy>=1.26.0
-seaborn>=0.13.0
-tqdm>=4.66.1
-scikit-learn>=1.3.1
-py-tgb>=0.9.0
-requests>=2.28.2
+requests>=2.28.2
+appdirs==1.4.4
+args==0.1.0
+certifi==2022.12.7
+charset_normalizer==3.1.0
+click==8.1.7
+clint==0.5.1
+contourpy==1.0.7
+cycler==0.11.0
+Cython==3.0.8
+decorator==4.4.2
+distlib==0.3.1
+docker-pycreds==0.4.0
+filelock==3.0.12
+fonttools==4.41.0
+geoopt==0.5.1
+gitdb==4.0.11
+GitPython==3.1.41
+idna==3.4
+importlib_resources==6.0.0
+Jinja2==3.1.2
+joblib==1.3.1
+kiwisolver==1.4.4
+MarkupSafe==2.1.2
+matplotlib==3.7.0
+more-itertools==8.7.0
+mpmath==1.3.0
+networkx==3.1
+numpy==1.24.2
+packaging==23.1
+pandas==1.5.3
+Pillow==9.5.0
+protobuf==4.25.2
+psutil==5.9.4
+py-tgb==0.9.2
+-e git+ssh://[email protected]/ComplexData-MILA/TGX.git@bf47a2946ef35c5f96ba8cbe9c3e397d39c5e58e#egg=py_tgx
+pyparsing==3.0.9
+python-dateutil==2.8.2
+pytz==2023.3
+PyYAML==6.0.1
+requests==2.28.2
+scikit_learn==1.3.0
+scipy==1.10.1
+seaborn==0.13.2
+sentry-sdk==1.40.3
+setproctitle==1.3.2
+setuptools-scm==6.0.1
+six==1.16.0
+sklearn==0.0
+smmap==5.0.1
+sympy==1.12
+threadpoolctl==3.2.0
+torch==2.0.1
+torch-geometric-temporal==0.54.0
+torch_geometric==2.3.1
+torch_scatter==2.1.1
+torch_sparse==0.6.17
+tqdm==4.65.0
+typing_extensions==4.7.1
+tzdata==2023.3
+urllib3==1.26.15
+virtualenv==20.0.18
+wandb==0.16.3
+zipp==3.15.0

setup.py

@@ -8,16 +8,29 @@ def readme():
 
 setup(
     name="py-tgx",
-    version="0.2.2",
-    description="Temporal graph",
-    long_description=readme(),
-    long_description_content_type="text/markdown",
-    url="https://github.com/fpour/tgx",
-    author="Razieh Shirzadkhani",
-    author_email="[email protected]",
-    keywords="Temporal Graph visualization",
+    version="0.3.1",
+    description="Temporal Graph Visualization with TGX",
+    url="https://github.com/ComplexData-MILA/TGX",
+    keywords="Temporal Graph Visualization",
     license="MIT",
     packages=find_packages(),
-    install_requires=[],
-    include_package_data=True,
+    install_requires=[
+        'appdirs',
+        'networkx',
+        'numpy',
+        'pandas',
+        'py-tgb',
+        'requests',
+        'scikit_learn',
+        'scipy',
+        'seaborn',
+        'sklearn',
+        'torch',
+        'torch-geometric-temporal',
+        'torch_geometric',
+        'torch_scatter',
+        'torch_sparse',
+        'tqdm',
+        'wandb',
+    ],
 )