Project Background:
The Graph Representation Learning encompasses the context and motivation for developing techniques that can effectively capture and utilize information from graph-structured data. Graphs are ubiquitous in various domains such as social networks, biological networks, recommendation systems, and knowledge graphs, representing entities (nodes) and their relationships (edges). Traditional machine learning algorithms are not directly applicable to graphs due to their non-Euclidean nature and the lack of fixed-dimensional representations. It also involves recognizing the limitations of traditional methods in handling graph data, such as feature engineering, computational complexity, and scalability issues. Graph Representation Learning aims to address these challenges by learning meaningful and low-dimensional representations of nodes and edges in graphs, allowing for efficient processing and analysis. These learned representations capture structural information, node attributes, and relational patterns, enabling downstream tasks such as node classification, link prediction, and graph clustering.
Problem Statement
- Graphs are complex data structures with nodes and edges representing entities and relationships, posing challenges for traditional machine learning algorithms to effectively model and extract useful information.
- Handling large-scale graphs with millions of nodes and edges requires scalable graph representation learning techniques that can capture meaningful patterns and structures efficiently.
- Manual feature engineering for graph data is labor-intensive and may not capture all relevant information, necessitating automated methods for learning expressive and informative representations.
- Graphs often contain heterogeneous information, including node attributes, edge types, and temporal dynamics, requiring models that can integrate and leverage these diverse sources of information.
- Ensuring that learned graph representations generalize well across different graph structures and domains, as well as facilitating transfer learning between related tasks, is a key challenge in graph representation learning.
Aim and Objectives
- Develop efficient and effective techniques for learning meaningful representations of nodes and edges in graph-structured data.
- Design scalable graph embedding algorithms that capture structural and relational information while reducing dimensionality.
- Explore methods for incorporating node attributes, edge features, and graph topology into learned representations for enhanced expressiveness.
- Evaluate the quality of learned representations on tasks such as node classification, link prediction, and graph clustering to assess their utility and generalization.
- Investigate techniques for handling heterogeneous graphs with diverse node types, edge types, and temporal dynamics, ensuring robustness across different data modalities.
- Facilitate transfer learning and knowledge transfer between related graphs or tasks, enabling the reuse of learned representations for improved model performance.
Contributions to Graph Representation Learning
- Introduce new graph embedding algorithms that improve the quality of learned representations, capturing complex structural and relational patterns in graph data.
- Develop scalable graph representation learning algorithms capable of handling large-scale graphs with millions of nodes and edges efficiently, without compromising on representation quality.
- Propose techniques for effectively handling heterogeneous graph data with diverse node types, edge types, and attributes, enabling more expressive and informative representations.
- Explore methods for transfer learning in graph representation learning, facilitating the transfer of knowledge and representations between related graphs or tasks to improve model performance and generalization.
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Develop graph representation learning techniques tailored to specific application domains such as social networks, bioinformatics, recommendation systems, or fraud detection, addressing domain-specific challenges and achieving state-of-the-art performance.
Deep Learning Algorithms for Graph Representation Learning
- Graph Convolutional Networks (GCNs)
- Graph Attention Networks (GATs)
- GraphSAGE (Graph Sample and Aggregate)
- Graph Convolutional Network with Attention Mechanism (GCN-Attention)
- Graph Isomorphism Network (GIN)
- DeepWalk
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Node2Vec
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Graph Autoencoder (GAE)
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Graph Neural Network (GNN)
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Graph Recurrent Neural Network (GRNN)
Datasets for Graph Representation Learning
- Cora
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Citeseer
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PubMed
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PPI (Protein-Protein Interaction)
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Reddit
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Amazon-Computers
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Amazon-Photo
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Coauthor-CS
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Coauthor-Physics
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DBLP
Performance Metrics
- Node Classification Accuracy
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Link Prediction Accuracy
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Graph Classification Accuracy
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Mean Average Precision (MAP)
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Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
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Area Under the Precision-Recall Curve (AUC-PR)
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F1 Score
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Precision
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Recall
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Mean Squared Error (MSE) for regression tasks
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
