docs: make Chinese README the default
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wehub-resource-sync
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<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/easy-graph/Easy-Graph) · [上游 README](https://github.com/easy-graph/Easy-Graph/blob/HEAD/README.md)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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EasyGraph
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==================
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Copyright (C) <2020-2026> by [DataNET Group, Fudan University](https://fudan-datanet.mysxl.cn/)
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Copyright (C) <2020-2026> by [DataNET Group，复旦大学](https://fudan-datanet.mysxl.cn/)
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___________________________________________________________________________
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@@ -26,10 +32,10 @@ ___________________________________________________________________________
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- **Youtube channel:** https://www.youtube.com/@python-easygraph
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# Introduction
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The framework of EasyGraph is composed of four components: **EasyGraph (Core)**, **EasyHypergraph**, **EGGPU**, and **EasyGNN**.
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EasyGraph 框架由四个组件构成：**EasyGraph (Core)**、**EasyHypergraph**、**EGGPU** 和 **EasyGNN**。
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**EasyGraph** is an open-source network analysis library primarily written in Python. It supports both undirected and directed networks and accommodates various network data formats. EasyGraph includes a comprehensive suite of network analysis algorithms such as community detection, structural hole spanner detection, network embedding, and motif detection. Additionally, it optimizes performance by implementing key components in C++ and utilizing multiprocessing.
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**EasyGraph** 是一款主要以 Python 编写的开源网络分析库。它同时支持无向网络和有向网络，并兼容多种网络数据格式。EasyGraph 提供一整套网络分析算法，包括社区发现、结构洞跨越者检测、网络嵌入和模体检测等。此外，它通过将关键组件以 C++ 实现并利用多进程来优化性能。
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<!-- # New Features in Version 1.3
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- **Support for more hypergraph metrics and algorithms.** Such as [hypercoreness](https://www.nature.com/articles/s41467-023-41887-2), [vector-centrality](https://www.sciencedirect.com/science/article/pii/S0960077922006075), [s-centrality](https://epjds.epj.org/articles/epjdata/abs/2020/01/13688_2020_Article_231/13688_2020_Article_231.html), and so on.
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@@ -37,19 +43,19 @@ The framework of EasyGraph is composed of four components: **EasyGraph (Core)**,
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- **Support for more flexible dynamic hypergraph visualization.** Users can define dynamic hypergraphs and visualize the structure of the hypergraph at each timestamp.
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- **Support for more efficient hypergraph computation and hypergraph learning.** Adoption of suitable storage structure and caching strategy for different metrics/hypergraph neural networks.
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-->
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👉 For more details, please refer to our [documentation](https://easy-graph.github.io/) page.
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👉 更多详情，请参阅我们的[文档](https://easy-graph.github.io/)页面。
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---
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**EasyHypergraph** is a comprehensive, computation-effective, and storage-saving hypergraph computation tool designed not only for in-depth hypergraph analysis but also for the growing field of hypergraph learning.
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It bridges the gap between EasyGraph and higher-order relationships. EasyHypergraph is developed as an integrated module within the EasyGraph framework, maintaining full compatibility with its core architecture.
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**EasyHypergraph** 是一款全面、计算高效且节省存储的超图计算工具，既适用于深入的 hypergraph 分析，也适用于日益发展的超图学习领域。
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它弥合了 EasyGraph 与高阶关系之间的鸿沟。EasyHypergraph 作为 EasyGraph 框架中的集成模块开发，与其核心架构完全兼容。
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👉 For more details, please refer to its [documentation](https://easy-graph.github.io/docs/hypergraph.html) page.
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👉 更多详情，请参阅其[文档](https://easy-graph.github.io/docs/hypergraph.html)页面。
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---
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**EGGPU** is a high-performance GPU-accelerated network analysis library that supports essential functions such as betweenness centrality, k-core centrality, and single-source shortest path,as well as structural hole metrics like constraint. Built on top of the EasyGraph library, EGGPU features an efficient system architecture and native CUDA implementation, while providing a user-friendly Python API and significant speedups for large-scale network analysis.
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**EGGPU** 是一款高性能 GPU 加速网络分析库，支持介数中心性、betweenness centrality）、k-core 中心性、单源最短路径等基础功能，以及约束（constraint）等结构洞指标。EGGPU 基于 EasyGraph 库构建，具备高效的系统架构和原生 CUDA 实现，同时提供友好的 Python API，可在大规模网络分析中带来显著加速。
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👉 For more details, please refer to its [documentation](https://easy-graph.github.io/docs/eggpu.html) page.
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👉 更多详情，请参阅其[文档](https://easy-graph.github.io/docs/eggpu.html)页面。
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# 📢 EasyGraph News
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@@ -121,7 +127,7 @@ If prebuilt EasyGraph wheels are not supported for your platform (OS / CPU arch,
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pip install ./Easy-Graph
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```
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#### On Windows
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#### 在 Windows 上
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```
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% For Windows users who want to enable GPU-based functions, %
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% you must execute the commands below in cmd but not PowerShell. %
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pip install ./Easy-Graph
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```
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#### On macOS
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#### 在 macOS 上
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```
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# Since macOS doesn't support CUDA, we can't have GPUs enabled on macOS
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git clone --recursive https://github.com/easy-graph/Easy-Graph
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pip install ./Easy-Graph
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```
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## Hint
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## 提示
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EasyGraph uses 1.12.1 <= [PyTorch](https://pytorch.org/get-started/locally/) < 2.0 for machine learning functions.
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Note that this does not prevent your from running non-machine learning functions normally, if there is no PyTorch in your environment.
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But you will receive some warnings which remind you some unavailable modules when they depend on it.
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EasyGraph 的机器学习功能要求 1.12.1 <= [PyTorch](https://pytorch.org/get-started/locally/) < 2.0。
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请注意，即使你的环境中未安装 PyTorch，这也不会阻止你正常运行非机器学习功能。
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但当某些模块依赖 PyTorch 时，你会收到一些警告，提示这些模块不可用。
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# Simple Example
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# 简单示例
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This example demonstrates the general usage of methods in EasyGraph.
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本示例演示 EasyGraph 中方法的常规用法。
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```python
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>>> import easygraph as eg
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>>> G = eg.Graph()
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>>> eg.pagerank(G)
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{1: 0.14272233049003707, 2: 0.14272233049003694, 3: 0.2685427766200994, 4: 0.14336430577918527, 5: 0.21634929087322705, 6: 0.0862989657474143}
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```
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This is a simple example for the detection of [structural hole spanners](https://en.wikipedia.org/wiki/Structural_holes)
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using the [HIS](https://keg.cs.tsinghua.edu.cn/jietang/publications/WWW13-Lou&Tang-Structural-Hole-Information-Diffusion.pdf) algorithm.
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这是一个使用 [HIS](https://keg.cs.tsinghua.edu.cn/jietang/publications/WWW13-Lou&Tang-Structural-Hole-Information-Diffusion.pdf) 算法检测 [结构洞跨越者（structural hole spanners）](https://en.wikipedia.org/wiki/Structural_holes)
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的简单示例。
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```python
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>>> import easygraph as eg
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@@ -170,9 +176,9 @@ using the [HIS](https://keg.cs.tsinghua.edu.cn/jietang/publications/WWW13-Lou&Ta
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6: {0: 0.83595703125}
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}
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```
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# Citation
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# 引用
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If you use EasyGraph in a scientific publication, we kindly request that you cite the following paper:
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如果你在学术出版物中使用 EasyGraph，恳请引用以下论文：
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```
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@article{gao2023easygraph,
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title={{EasyGraph: A Multifunctional, Cross-Platform, and Effective Library for Interdisciplinary Network Analysis}},
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@@ -184,4 +190,4 @@ If you use EasyGraph in a scientific publication, we kindly request that you cit
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pages={100839},
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}
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```
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📢 If you notice anything unexpected, please open an issue and let us know. If you have any questions or require a specific feature, feel free to discuss them with us. We are motivated to constantly make EasyGraph even better and let more developers benefit!
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📢 如果你发现任何异常情况，请提交 issue 告知我们。如有任何问题或需要特定功能，欢迎与我们讨论。我们致力于持续改进 EasyGraph，让更多开发者受益！
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