From bd28e23c1e94095d32ddd1154402c778a975f045 Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:26:34 +0000 Subject: [PATCH] docs: make Chinese README the default --- README.md | 50 ++++++++++++++++++++++++++++---------------------- 1 file changed, 28 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index fb9aeb8..5093bbb 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,13 @@ + +> [!NOTE] +> 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。 +> [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) +> 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。 + EasyGraph ================== -Copyright (C) <2020-2026> by [DataNET Group, Fudan University](https://fudan-datanet.mysxl.cn/) +Copyright (C) <2020-2026> by [DataNET Group，复旦大学](https://fudan-datanet.mysxl.cn/) ___________________________________________________________________________ @@ -26,10 +32,10 @@ ___________________________________________________________________________ - **Youtube channel:** https://www.youtube.com/@python-easygraph # Introduction -The framework of EasyGraph is composed of four components: **EasyGraph (Core)**, **EasyHypergraph**, **EGGPU**, and **EasyGNN**. +EasyGraph 框架由四个组件构成：**EasyGraph (Core)**、**EasyHypergraph**、**EGGPU** 和 **EasyGNN**。 ![Framework of EasyGraph.](EG_framework.png) -**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. +**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。 -👉 For more details, please refer to our [documentation](https://easy-graph.github.io/) page. +👉 更多详情，请参阅我们的[文档](https://easy-graph.github.io/)页面。 --- -**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. -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. +**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。 +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。 -👉 For more details, please refer to its [documentation](https://easy-graph.github.io/docs/hypergraph.html) page. +👉 更多详情，请参阅其[文档](https://easy-graph.github.io/docs/hypergraph.html)页面。 --- -**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. +**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。 -👉 For more details, please refer to its [documentation](https://easy-graph.github.io/docs/eggpu.html) page. +👉 更多详情，请参阅其[文档](https://easy-graph.github.io/docs/eggpu.html)页面。 # 📢 EasyGraph News @@ -121,7 +127,7 @@ If prebuilt EasyGraph wheels are not supported for your platform (OS / CPU arch, pip install ./Easy-Graph ``` -#### On Windows +#### 在 Windows 上 ``` % For Windows users who want to enable GPU-based functions, % % you must execute the commands below in cmd but not PowerShell. % @@ -130,22 +136,22 @@ If prebuilt EasyGraph wheels are not supported for your platform (OS / CPU arch, pip install ./Easy-Graph ``` -#### On macOS +#### 在 macOS 上 ``` # Since macOS doesn't support CUDA, we can't have GPUs enabled on macOS git clone --recursive https://github.com/easy-graph/Easy-Graph pip install ./Easy-Graph ``` -## Hint +## 提示 -EasyGraph uses 1.12.1 <= [PyTorch](https://pytorch.org/get-started/locally/) < 2.0 for machine learning functions. -Note that this does not prevent your from running non-machine learning functions normally, if there is no PyTorch in your environment. -But you will receive some warnings which remind you some unavailable modules when they depend on it. +EasyGraph 的机器学习功能要求 1.12.1 <= [PyTorch](https://pytorch.org/get-started/locally/) < 2.0。 +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。 +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。 -# Simple Example +# 简单示例 -This example demonstrates the general usage of methods in EasyGraph. +本示例演示 EasyGraph 中方法的常规用法。 ```python >>> import easygraph as eg >>> G = eg.Graph() @@ -153,8 +159,8 @@ This example demonstrates the general usage of methods in EasyGraph. >>> eg.pagerank(G) {1: 0.14272233049003707, 2: 0.14272233049003694, 3: 0.2685427766200994, 4: 0.14336430577918527, 5: 0.21634929087322705, 6: 0.0862989657474143} ``` -This is a simple example for the detection of [structural hole spanners](https://en.wikipedia.org/wiki/Structural_holes) -using the [HIS](https://keg.cs.tsinghua.edu.cn/jietang/publications/WWW13-Lou&Tang-Structural-Hole-Information-Diffusion.pdf) algorithm. +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) +的简单示例。 ```python >>> import easygraph as eg @@ -170,9 +176,9 @@ using the [HIS](https://keg.cs.tsinghua.edu.cn/jietang/publications/WWW13-Lou&Ta 6: {0: 0.83595703125} } ``` -# Citation +# 引用 -If you use EasyGraph in a scientific publication, we kindly request that you cite the following paper: +如果你在学术出版物中使用 EasyGraph，恳请引用以下论文： ``` @article{gao2023easygraph, title={{EasyGraph: A Multifunctional, Cross-Platform, and Effective Library for Interdisciplinary Network Analysis}}, @@ -184,4 +190,4 @@ If you use EasyGraph in a scientific publication, we kindly request that you cit pages={100839}, } ``` -📢 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! +📢 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！