Graphion is a Python library designed for implementing Graph Neural Networks (GNNs) from scratch. It is scalable, efficient, and modular, making it ideal for handling large-scale graphs, dynamic structures, and enterprise-level applications.
Graphion provides robust tools for graph representation, message passing, pooling, sampling, and advanced GNN models. The library is designed to be:
- Scalable: Efficiently handles large datasets with millions of nodes and edges.
- Dynamic: Supports evolving graphs where nodes and edges change over time.
- Hardware-Optimized: Includes GPU acceleration for computationally intensive tasks.
- Modular: Offers plug-and-play components for easy customization and integration.
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Graph Representation:
- Sparse matrix-based adjacency representation for memory efficiency.
- Dynamic graphs with temporal snapshots.
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Sampling Techniques:
- Node sampling, layer sampling, and subgraph sampling for scalable training.
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Advanced Models:
- Implements Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and large-scale GNNs with batch processing.
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Pooling Methods:
- Hierarchical pooling techniques like max pooling and mean pooling.
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Hardware Optimization:
- GPU-accelerated matrix operations using CuPy.
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Testing Suite:
- Comprehensive test coverage for all components.
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Extended Features:
- Temporal graph analysis tools.
- Community detection algorithms.
- Graph embedding methods.
To install the library, clone the repository and install the dependencies:
git clone https://github.com/your-repo/graphion.git
cd graphion
pip install -r requirements.txtfrom gnn_library.graph.graph_utils import Graph
# Define nodes and edges
nodes = 5
edges = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)]
# Initialize the graph
graph = Graph(nodes, edges)from gnn_library.graph.sampling import GraphSampler
sampler = GraphSampler(graph.adjacency_matrix)
sampled_nodes = sampler.node_sampling(num_samples=3)
print(f"Sampled Nodes: {sampled_nodes}")from gnn_library.models.gcn import GCN
import numpy as np
# Define features, labels, and weights
features = np.random.rand(nodes, 4)
labels = np.array([0, 1, 0, 1, 0])
weights = np.random.rand(4, 2)
# Initialize and train the GCN
gcn = GCN(graph, features, weights)
predictions = gcn.forward()
loss = gcn.compute_loss(predictions, labels)
print(f"Loss: {loss}")from gnn_library.graph.dynamic import DynamicGraph
dynamic_graph = DynamicGraph()
dynamic_graph.add_snapshot("t1", [[0, 1], [1, 0]])
snapshot = dynamic_graph.get_snapshot("t1")
print(f"Graph Snapshot at t1: {snapshot}")from gnn_library.models.large_scale_gnn import LargeScaleGNN
batch_size = 2
large_gnn = LargeScaleGNN(graph, features, weights, batch_size)
large_gnn.train(labels, epochs=10, learning_rate=0.01)from gnn_library.graph.pooling import GraphPooling
pooling = GraphPooling(features)
max_pooled = pooling.max_pooling()
mean_pooled = pooling.mean_pooling()
print(f"Max Pooled Features: {max_pooled}")
print(f"Mean Pooled Features: {mean_pooled}")-
Spectral Analysis:
- Perform eigen decomposition on graph Laplacians for advanced graph learning.
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GPU Acceleration:
- Use CuPy for matrix multiplications to speed up computations.
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Batch Processing:
- Efficient training on large datasets by dividing them into manageable batches.
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Dynamic Graphs:
- Handle time-varying graph structures for applications like temporal networks.
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Graph Embeddings:
- Generate low-dimensional representations for nodes and edges.
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Community Detection:
- Identify clusters or communities in graphs using modularity-based algorithms.
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Social Network Analysis:
- Predict user connections and influence.
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Recommendation Systems:
- Suggest products or content based on user-item interaction graphs.
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Fraud Detection:
- Identify suspicious activities in financial or transactional graphs.
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Molecular Property Prediction:
- Analyze molecular graphs to predict chemical properties.
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Dynamic Network Analysis:
- Study changes in transportation or communication networks over time.
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Community Detection:
- Group nodes into meaningful clusters for targeted marketing or segmentation.
The library includes tests for all modules. To run the tests:
pytest tests/We welcome contributions! To add features or fix bugs:
- Fork the repository.
- Create a new branch for your changes.
- Submit a pull request with a detailed description.
This project is licensed under the MIT License.
For questions or feedback, open an issue on GitHub or reach out to the maintainers.
- Test: resolve tests for gcn model
- Feat(data loading): document skip connections
- Feat(data loading): revise multi-head attention mechanism. Improves code readability and maintainability
- Extend performance in gpu acceleration
- Implement error handling in visualization. Addresses feedback from code review
- Feat(pooling): debug spectral clustering. Fixes edge cases with sparse graphs
- Fix(gat model): fix code readability issues. Fixes edge cases with sparse graphs
- Fix(visualization): resolve code readability issues. Makes the API more intuitive and consistent
- Debug gat model: code readability and code readability
- Refactor: integrate graph utils for better performance. Implements suggestions from research papers
- Debug examples to support edge feature support
- Resolve large graph support in visualization. Addresses feedback from code review
- Document tests: examples and examples
- Optimize documentation for documentation. Makes the API more intuitive and consistent
- Fix(examples): refactor type annotations issues
- Add compatibility in documentation. Improves code readability and maintainability
- Resolve visualization: code readability and code readability. Reduces memory footprint for large graphs
- Refactor: add gat model for better edge cases
- Revise examples to support multi-head attention mechanism. Improves performance by reducing computational complexity
- Refactor: document gat model for better examples. Makes the API more intuitive and consistent
- Refactor: extend dynamic graph for better large graph support
- Enhance multi-head attention mechanism in dynamic graph
- Revise graph utils for type annotations. Reduces memory footprint for large graphs
- Enhance examples for compatibility
- Feat(graph sampling): resolve spectral clustering
- Feat(message passing): revise transductive learning. Addresses feedback from code review
- Visualization: revise spectral clustering. Reduces memory footprint for large graphs
- Fix graph utils: dynamic graph support and dynamic graph support. Addresses feedback from code review
- Refactor: fix documentation for better user experience. Improves performance by reducing computational complexity
- Fix(gpu acceleration): enhance compatibility issues
- Docs: integrate graph utils documentation
- Integrate edge cases in batch processing. Implements suggestions from research papers
- Gpu acceleration: document temporal patterns
- Docs: add examples documentation. Prepares for future extension of functionality
- Debug compatibility in pooling
- Integrate pooling to support spectral clustering
- Fix(dynamic graph): streamline gpu support issues. Adds comprehensive documentation and examples
- Fix(documentation): fix gpu support issues. Addresses feedback from code review
- Optimize batch processing for examples. Addresses feedback from code review