Project Background:
Graph Convolutional Networks (GCNs) have emerged as a powerful tool for learning from graph-structured data, where nodes represent entities and edges capture relationships between them. The project lies in addressing complex problems such as node classification, link prediction, and graph-level tasks within this framework. GCNs leverage graph convolution operations to aggregate information from neighboring nodes, enabling them to capture both local and global dependencies in the graph. This project aims to advance the understanding and application of GCNs by exploring novel architectures, optimization techniques, and applications in diverse domains such as social networks, biology, and recommendation systems. By harnessing the expressive power of GCNs, the project work seeks to achieve state-of-the-art performance in tasks requiring effective modeling of relational data, contributing to advancements in machine learning and artificial intelligence research.
Problem Statement
- GCNs is scalability when dealing with large-scale graphs. As the size of the graph increases, the computational and memory requirements of GCNs also grow, making it challenging to train and deploy these models efficiently.
- GCNs often struggle to generalize well across diverse graphs with varying structures and characteristics. Models trained on one type of graph may not perform optimally on another, leading to issues in model robustness and adaptability.
- Another issue is over-smoothing, where GCNs may lose important structural information and node-specific features during the aggregation process. This can result in reduced performance on tasks requiring fine-grained node-level predictions or distinctions.
- Dynamic graphs that evolve over time pose a challenge for GCNs, as these models need to adapt to changes in the graph structure while retaining learned information. Ensuring continuous learning and updating of GCNs in such dynamic environments is a non-trivial problem.
Aim and Objectives
- The aim of GCNs is to develop effective models for learning from graph-structured data by leveraging graph convolution operations.
- Develop scalable GCN architectures capable of handling large-scale graphs efficiently.
- Improve generalization capabilities of GCNs across diverse graph structures and characteristics.
- Mitigate over-smoothing effects in GCNs to preserve important structural and node-specific information.
- Explore techniques for adapting GCNs to dynamic graphs, enabling continuous learning and updating in evolving environments.
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Investigate optimization methods to enhance the training and inference speed of GCNs without compromising performance.
Contributions to Graph Convolutional Networks
- Proposed novel graph convolutional architectures that enhance scalability and generalization.
- Developed techniques to mitigate over-smoothing and preserve important structural information.
- Explored adaptation strategies for GCNs in dynamic graph environments, ensuring continuous learning.
- Contributed optimization methods to improve training efficiency and inference speed of GCNs.
Deep Learning Algorithms for Graph Convolutional Networks
- Graph Convolutional Network (GCN)
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Graph Attention Network (GAT)
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GraphSAGE (Graph Sample and Aggregation)
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Graph Isomorphism Network (GIN)
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Deep Graph Infomax (DGI)
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Graph Neural Network (GNN)
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Relational Graph Convolutional Network (R-GCN)
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Graph Transformer Network
Datasets for Graph Convolutional Networks
- Cora
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Citeseer
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Pubmed
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Reddit
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Amazon Product Co-Purchasing Network
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PPI (Protein-Protein Interaction)
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Yelp Social Network
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BlogCatalog
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Flickr
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Enzymes
Performance Metrics
- Accuracy
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Precision
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Recall
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F1-score
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Area Under the ROC Curve (AUC-ROC)
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Mean Absolute Error (MAE)
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Mean Squared Error (MSE)
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Mean Average Precision (MAP)
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Top-k Accuracy
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Normalized Mutual Information (NMI)
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
