ALL IN ONE: MULTI-TASK PROMPTING FOR GRAPH NEURAL NETWORKS

Abstract

Graph Neural Networks (GNNs) have demonstrated exceptional performance across a wide range of graph-based tasks, such as node classification, link prediction, and graph classification. However, existing approaches often address individual tasks in isolation, leading to inefficiencies in leveraging shared knowledge across tasks. This project introduces a unified framework, “ALL IN ONE: Multi-Task Prompting for Graph Neural Networks,” which employs multi-task learning and task-specific prompting to improve the adaptability and efficiency of GNNs. By designing specialized prompts for each task, the system enables effective knowledge sharing and task-specific optimization within a single GNN framework, reducing computational overhead and improving performance across diverse tasks.

Introduction

Graph Neural Networks are pivotal for processing graph-structured data in domains such as social networks, biology, and recommendation systems. Despite their versatility, the lack of unified frameworks for handling multiple tasks simultaneously limits their scalability and reusability. Multi-task learning presents a promising avenue by leveraging shared representations across tasks, but task interference and suboptimal task-specific learning remain challenges. This project proposes a novel prompting approach where multi-task learning is augmented with tailored task prompts to ensure efficient and context-specific GNN adaptation, fostering both task-specific excellence and knowledge transfer.

Existing System

Traditional GNNs address individual tasks such as node classification, link prediction, or graph-level classification independently. Key limitations include:

1. Task Isolation: Separate training for each task ignores shared patterns and representations across tasks.
2. Resource Overhead: Training multiple GNNs for different tasks increases computational cost.
3. Suboptimal Generalization: Independent models lack the ability to generalize across related tasks, leading to overfitting and inefficiency.
4. Static Architectures: Existing systems do not dynamically adapt to the varying requirements of different tasks.

Proposed System

The proposed system introduces a Multi-Task Prompting Framework for GNNs that integrates multiple tasks into a single model using task-specific prompts. Key features include:

1. Task-Specific Prompts: Employ customized prompts to guide the GNN’s focus for each task.
2. Shared Representation Learning: Enable efficient knowledge sharing across tasks through a common backbone network.
3. Dynamic Adaptation: Incorporate mechanisms to dynamically adapt the model for diverse task requirements.
4. Unified Pipeline: Consolidate multiple tasks into a single framework, reducing computational overhead and improving scalability.

Methodology

1. Data Preparation:
   • Collect graph-based datasets representing various tasks (e.g., node classification, graph classification, link prediction).
   • Preprocess data for uniformity, including adjacency matrices and node feature normalization.
2. Prompt Design:
   • Define task-specific prompts that encode task-specific objectives and priorities.
   • Incorporate prompts as auxiliary input to the GNN architecture.
3. Shared GNN Backbone:
   • Utilize a base GNN model (e.g., Graph Convolutional Networks, Graph Attention Networks) as the backbone.
   • Train the backbone to learn generalized graph representations.
4. Multi-Task Learning:
   • Use a multi-task loss function that balances task-specific performance and shared representation optimization.
   • Leverage techniques like gradient blending or task-specific attention layers to minimize task interference.
5. Evaluation:
   • Assess performance using task-specific metrics (e.g., accuracy for node classification, AUC for link prediction).
   • Compare against single-task GNNs and multi-task baselines.

Technologies Used

1. Programming Languages: Python
2. Frameworks: PyTorch Geometric, DGL (Deep Graph Library)
3. Libraries: Scikit-learn, NumPy, Pandas
4. GNN Models: GCN (Graph Convolutional Network), GAT (Graph Attention Network), GraphSAGE
5. Visualization Tools: TensorBoard, Matplotlib
6. Hardware: GPU-enabled systems for accelerated training (e.g., NVIDIA CUDA).
7. Data Storage: SQL or NoSQL databases for managing graph-structured data.