计图挑战赛赛道一热身赛：Cora GCN 节点分类

本仓库是战队“雪国”的计图挑战赛赛道一热身赛开源项目，使用 Jittor 和 JittorGeometric 训练两层 GCN，完成 Cora 引文网络节点分类。基础方案线上测试准确率为 0.821。

• GitLink：jinchi_NKU/jittor-雪国-计图挑战赛赛道一热身赛
• 完整项目说明：release/README.md
• 核心代码：release/gcn.py
• 改进代码：release/gcn_improved.py

环境安装

已验证环境为 Ubuntu 22.04、Python 3.10.12、Jittor 1.3.11.0。最小依赖及 WSL2 安装方法见完整项目说明。

python -m pip install jittor==1.3.11.0 numpy==1.24.0 pyparsing==3.2.1 astunparse pillow six tqdm
export PYTHONPATH="$PWD:$PYTHONPATH"

数据准备

从比赛发布包获取 cora.pkl，放置于 release/data/cora.pkl。数据集原始文件不包含在开源仓库中。

训练

export nvcc_path=""  # CPU 环境
python release/gcn.py

多随机种子改进版：

python release/gcn_improved.py

评测与推理

训练脚本会计算验证集准确率、加载最佳权重并自动生成测试集预测。基础版输出 release/result.json，改进版输出 release/result_improved.json 和可提交压缩包。

结果说明

评测指标为节点分类准确率，即预测正确的测试节点数除以测试节点总数。基础方案验证集准确率为 0.8120，线上测试准确率为 0.821。因测试标签隐藏，线上准确率需要在比赛平台提交后获得。

可复现说明

基础方案固定随机种子 42；改进版固定 5 个随机种子。完整环境、参数、数据格式、输出格式及复现命令见 release/README.md。

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上游项目：JittorGeometric 2.0

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Overview

JittorGeometric 2.0 is a state-of-the-art graph machine learning library built on the Jittor framework. As a Chinese-developed deep learning library, JittorGeometric provides comprehensive support for Graph Neural Networks (GNNs) research and applications, featuring enhanced performance, flexibility, and scalability.

🌟 Key Features

Core Capabilities

• 🚀 JIT Compilation: Leverage Just-In-Time compilation for dynamic code modification without pre-compilation overhead
• ⚡ Optimized Sparse Operations: High-performance sparse matrix computations with CuSparse acceleration
• 🎯 Comprehensive Model Zoo: 40+ implemented models covering classic, spectral, dynamic, molecular, and transformer-based GNNs
• 📊 Rich Dataset Support: Built-in loaders for popular graph datasets (Planetoid, OGB, Reddit, etc.)

New in Version 2.0

• 🔄 Distributed Training: Multi-GPU and multi-node training support with MPI
• 🌊 Dynamic Graph Processing: Event-based dynamic graph support with parallel processing
• 📦 Mini-batch Support: Efficient mini-batch training for large-scale graphs
• 🔧 Ascend-GNN: GNN for NPU
• 🎛️ Extended Model Categories: Graph transformers, self-supervised learning, and recommendation systems

Quick Tour

### Dataset Selection
import os.path as osp
from jittor_geometric.datasets import Planetoid
import jittor_geometric.transforms as T
import jittor as jt

dataset = 'cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data', dataset)
dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures())
data = dataset[0]
v_num = data.x.shape[0]

### Data Preprocess
from jittor_geometric.ops import cootocsr,cootocsc
from jittor_geometric.nn.conv.gcn_conv import gcn_norm
edge_index, edge_weight = data.edge_index, data.edge_attr
edge_index, edge_weight = gcn_norm(
                        edge_index, edge_weight,v_num,
                        improved=False, add_self_loops=True)
with jt.no_grad():
   data.csc = cootocsc(edge_index, edge_weight, v_num)
   data.csr = cootocsr(edge_index, edge_weight, v_num)

### Model Definition
from jittor import nn
from jittor_geometric.nn import GCNConv

class GCN(nn.Module):
    def __init__(self, dataset, dropout=0.8):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(in_channels=dataset.num_features, out_channels=256)
        self.conv2 = GCNConv(in_channels=256, out_channels=dataset.num_classes)
        self.dropout = dropout

    def execute(self):
        x, csc, csr = data.x, data.csc, data.csr
        x = nn.relu(self.conv1(x, csc, csr))
        x = nn.dropout(x, self.dropout, is_train=self.training)
        x = self.conv2(x, csc, csr)
        return nn.log_softmax(x, dim=1)

### Training
model = GCN(dataset)
optimizer = nn.Adam(params=model.parameters(), lr=0.001, weight_decay=5e-4) 
for epoch in range(200):
   model.train()
   pred = model()[data.train_mask]
   label = data.y[data.train_mask]
   loss = nn.nll_loss(pred, label)
   optimizer.step(loss)

Supported Models

JittorGeometric 2.0 includes implementations of 40+ state-of-the-art GNN models:

Classic Graph Neural Networks

Model	Year	Venue	Description
ChebNet	2016	NeurIPS	Spectral graph convolutions
GCN	2017	ICLR	Graph Convolutional Networks
GraphSAGE	2017	NeurIPS	Inductive graph learning
GAT	2018	ICLR	Graph Attention Networks
SGC	2019	ICML	Simplified Graph Convolution
APPNP	2019	ICLR	Approximate Personalized Propagation
GCNII	2020	ICML	Deeper Graph Convolutional Networks

Spectral Graph Neural Networks

Model	Year	Venue	Description
GPRGNN	2021	ICLR	Generalized PageRank GNN
BernNet	2021	NeurIPS	Bernstein polynomial filters
ChebNetII	2022	NeurIPS	Improved Chebyshev filters
EvenNet	2022	NeurIPS	Even polynomial filters
OptBasis	2023	ICML	Optimal basis functions

Dynamic Graph Neural Networks

Model	Year	Venue	Description
JODIE	2019	SIGKDD	Temporal interaction networks
DyRep	2019	ICLR	Dynamic representation learning
TGN	2020	ArXiv	Temporal Graph Networks
GraphMixer	2022	ICLR	MLP-based dynamic graphs
Dygformer	2023	NeurIPS	Dynamic graph transformers

Molecular Graph Neural Networks

Model	Year	Venue	Description
SchNet	2017	NeurIPS	Continuous-filter convolutions
DimeNet	2020	ICLR	Directional message passing
EGNN	2021	ICML	Equivariant Graph Networks
Graphormer	2021	NeurIPS	Graph transformers for molecules
SphereNet	2022	ICLR	Spherical message passing
Uni-Mol	2023	ICLR	Universal molecular representation
Transformer-M	2023	ICLR	Molecular transformers

Graph Self-supervised Learning

Model	Year	Venue	Description
DGI	2019	ICLR	Deep Graph Infomax
MVGRL	2020	ICML	Multi-view contrastive learning
GRACE	2020	ICML	Graph contrastive learning
PolyGCL	2024	ICLR	Polynomial graph contrastive learning

Graph Recommendation

Model	Year	Venue	Description
SASREC	2018	ICDM	Self-attentive sequential recommendation
SGNNHN	2020	CIKM	Set-based GNN for heterogeneous networks
CRAFT	2025	NeurIPS	Cross-attention recommendation

Graph Embedding

Model	Year	Venue	Description
Deepwalk	2014	KDD	Random walk embeddings
LINE	2015	WWW	Large-scale information network embedding
Node2Vec	2016	KDD	Scalable feature learning
LightGCN	2020	SIGIR	Simplified GCN for recommendation
DirectAU	2022	KDD	Direct alignment and uniformity
SimGCL	2024	KAIS	Simple graph contrastive learning
XSimGCL	2024	TKDE	Extreme simple graph contrastive learning

Graph Transformers

Model	Year	Venue	Description
SGFormer	2023	NeurIPS	Simplifying graph transformers
NAGFormer	2023	ICLR	Neighborhood aggregation transformers
PolyFormer	2024	KDD	Polynomial-based graph transformers

Installation

Step-by-Step Installation

1. Create a conda environment

   conda create -n jittorgeometric python=3.10
   conda activate jittorgeometric

2. Install Jittor

   python -m pip install git+https://github.com/Jittor/jittor.git

   or follow the Jittor official documentation.

3. Install dependencies

   pip install astunparse==1.6.3 autograd==1.7.0 cupy==13.3.0 numpy==1.24.0 \
               pandas==2.2.3 Pillow==11.1.0 PyMetis==2023.1.1 six==1.16.0 \
               pyparsing==3.2 scipy==1.15.1 setuptools==69.5.1 sympy==1.13.3 \
               tqdm==4.66.4 einops huggingface_hub==0.27.1 networkx==3.4.2 \
               scikit-learn==1.7.1 rdkit==2025.3.5 seaborn==0.13.2 \
               alive-progress==3.3.0

4. Install JittorGeometric

   git clone https://github.com/AlgRUC/JittorGeometric.git
   cd JittorGeometric
   pip install .

5. Verify installation

   python examples/gcn_example.py

For Distributed Training (Optional)

Install MPI support:

conda install -c conda-forge openmpi=4.0.5
conda install -c conda-forge mpi4py

📋 Requirements

• Python 3.10+
• CUDA 11.0+ (for GPU support)
• Jittor 1.3.0+
• CuPy (for CUDA operations)
• NumPy, SciPy, NetworkX
• For distributed training: OpenMPI 4.0.5+ and mpi4py

🔄 Distributed Training

JittorGeometric 2.0 supports distributed training across multiple GPUs and nodes:

Single Machine Multi-GPU

mpiexec -n 2 python dist_gcn.py --num_parts 2 --dataset reddit

Multi-Node Training

1. Configure your hostfile: ```

2. 31.195.15 slots=1

3. 31.195.16 slots=1 ```

4. Partition the graph:

   python dist_partition.py --dataset reddit --num_parts 2 --use_gdc

5. Launch distributed training: ```bash mpirun -n 2 –hostfile hostfile \

–prefix /path/to/conda/env
python dist_gcn.py –num_parts 2 –dataset reddit

For detailed distributed training setup, see [examples/README.md](/jinchi_NKU/jittor-geometric-competition/tree/main/examples/README.md).

## 🧪 Testing
Run a specific example:
```bash
python examples/gcn_example.py

📖 Documentation

Comprehensive documentation is available at https://algruc.github.io/JittorGeometric/index.html

👥 Contributors

This project is actively maintained by the JittorGeometric Team at Renmin University of China and Northeastern University‌.

Core Team

• Project Lead: runlin_lei@ruc.edu.cn
• Contributors: See Contributors

📄 License

JittorGeometric is released under the Apache 2.0 License.

🙏 Acknowledgments

• Jittor Team for the deep learning framework
• PyTorch Geometric for inspiration
• All contributors and users of JittorGeometric

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