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| Original file line number | Diff line number | Diff line change |
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| # Welcome to TGX | ||
| <!-- # TGX --> | ||
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| # Temporal Graph Analysis with TGX (WSDM 2024 Demo Track) | ||
| <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> | ||
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| 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 | ||
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| ### Pip Install | ||
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| You can install TGX via [pip](https://pypi.org/project/py-tgb/) | ||
| ### 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) | ||
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| 1. Load TGB datasets | ||
| ``` | ||
| pip install py-tgx | ||
| import tgx | ||
| dataset = tgx.tgb_data("tgbl-wiki") | ||
| ctdg = tgx.Graph(dataset) | ||
| ``` | ||
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| <!-- ### Links and Datasets | ||
| <!-- The project website can be found [here](https://tgb.complexdatalab.com/). --> | ||
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| <!-- The API documentations can be found [here](https://shenyanghuang.github.io/TGB/). --> | ||
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| <!-- all dataset download links can be found at [info.py](https://github.com/shenyangHuang/TGB/blob/main/tgb/utils/info.py) | ||
| TGB dataloader will also automatically download the dataset as well as the negative samples for the link property prediction datasets. --> | ||
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| <!-- ### Install dependency --> | ||
| ### Install dependency | ||
| Our implementation works with python >= 3.9 and can be installed as follows | ||
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| 1. set up virtual environment (conda should work as well) | ||
| 2. Load built-in datasets | ||
| ``` | ||
| python -m venv ~/tgx_env/ | ||
| source ~/tgx_env/bin/activate | ||
| dataset = tgx.builtin.uci() | ||
| ctdg = tgx.Graph(dataset) | ||
| ``` | ||
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| 2. install external packages | ||
| 3. Load custom datasets from `.csv` | ||
| ``` | ||
| pip install -r requirements.txt | ||
| 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) | ||
| ``` | ||
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| ### 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) | ||
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| 1. Discretize the network (required for viz) | ||
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| 3. install local dependencies under root directory `/TGX` | ||
| ``` | ||
| pip install -e py-tgx | ||
| dataset = tgx.builtin.uci() | ||
| ctdg = tgx.Graph(dataset) | ||
| time_scale = "weekly" | ||
| dtdg, ts_list = ctdg.discretize(time_scale=time_scale, store_unix=True) | ||
| ``` | ||
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| 2. Plot the number of nodes over time | ||
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| <!-- ### Instruction for tracking new documentation and running mkdocs locally | ||
| 1. first run the mkdocs server locally in your terminal | ||
| ``` | ||
| mkdocs serve | ||
| tgx.degree_over_time(dtdg, network_name="uci") | ||
| ``` | ||
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| 2. go to the local hosted web address similar to | ||
| 3. Compute novelty index | ||
| ``` | ||
| [14:18:13] Browser connected: http://127.0.0.1:8000/ | ||
| ``` --> | ||
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| <!-- Example: to track documentation of a new hi.py file in tgb/edgeregression/hi.py --> | ||
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| <!-- 3. create docs/api/tgb.hi.md and add the following | ||
| tgx.get_novelty(dtdg) | ||
| ``` | ||
| # `tgb.edgeregression` | ||
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| ::: tgb.edgeregression.hi | ||
| ``` --> | ||
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| <!-- 4. edit mkdocs.yml | ||
| ``` | ||
| nav: | ||
| - Overview: index.md | ||
| - About: about.md | ||
| - API: | ||
| other *.md files | ||
| - tgb.edgeregression: api/tgb.hi.md | ||
| ``` --> | ||
| ### Install dependency | ||
| Our implementation works with python >= 3.9 and can be installed as follows | ||
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| ### Creating new branch ### | ||
| 1. set up virtual environment (conda should work as well) | ||
| ``` | ||
| git fetch origin | ||
| git checkout -b test origin/test | ||
| python -m venv ~/tgx_env/ | ||
| source ~/tgx_env/bin/activate | ||
| ``` | ||
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| ### dependencies for mkdocs (documentation) | ||
| 2. install external packages | ||
| ``` | ||
| pip install mkdocs | ||
| pip install mkdocs-material | ||
| pip install mkdocstrings-python | ||
| pip install mkdocs-jupyter | ||
| pip install notebook | ||
| pip install mkdocs-glightbox | ||
| pip install -r requirements.txt | ||
| ``` | ||
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| ### full dependency list | ||
| Our implementation works with python >= 3.9 and has the following dependencies | ||
| 3. install local dependencies under root directory `/TGX` | ||
| <!-- ``` | ||
| pip install -e py-tgx | ||
| ``` --> | ||
| ``` | ||
| 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 | ||
| tgb==0.9.0 | ||
| pip install -e . | ||
| ``` | ||
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| <!-- ## Code blocks | ||
| `pip install tgb` --> | ||
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| <!-- | ||
| ### Plain codeblock | ||
| A plain codeblock: | ||
| 3. [alternatively] install from test-pypi | ||
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| ``` | ||
| Some code here | ||
| def myfunction() | ||
| // some comment | ||
| 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 | ||
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| #### Code for a specific language | ||
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| Some more code with the `py` at the start: | ||
| ``` py | ||
| import tensorflow as tf | ||
| def whatever() | ||
| 4. [optional] install mkdocs dependencies to serve the documentation locally | ||
| ``` | ||
| #### With a title | ||
| ``` py title="bubble_sort.py" | ||
| def bubble_sort(items): | ||
| for i in range(len(items)): | ||
| for j in range(len(items) - 1 - i): | ||
| if items[j] > items[j + 1]: | ||
| items[j], items[j + 1] = items[j + 1], items[j] | ||
| pip install mkdocs-glightbox | ||
| ``` | ||
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| #### With line numbers | ||
| ### Creating new branch ### | ||
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| ``` py linenums="1" | ||
| def bubble_sort(items): | ||
| for i in range(len(items)): | ||
| for j in range(len(items) - 1 - i): | ||
| if items[j] > items[j + 1]: | ||
| items[j], items[j + 1] = items[j + 1], items[j] | ||
| first create the branch on github | ||
| ``` | ||
| git fetch origin | ||
| #### Highlighting lines | ||
| ``` py hl_lines="2 3" | ||
| def bubble_sort(items): | ||
| for i in range(len(items)): | ||
| for j in range(len(items) - 1 - i): | ||
| if items[j] > items[j + 1]: | ||
| items[j], items[j + 1] = items[j + 1], items[j] | ||
| git checkout -b test origin/test | ||
| ``` | ||
| ## Icons and Emojs | ||
| :smile: | ||
| :fontawesome-regular-face-laugh-wink: | ||
| :fontawesome-brands-twitter:{ .twitter } | ||
| :octicons-heart-fill-24:{ .heart } --> |
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