Publications

Research contributions in Graph Neural Networks, focusing on expressivity, efficiency, and practical applications

FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening

Shubhajit Roy, Hrriday Ruparel, Kishan Ved, Anirban Dasgupta • 2024

Arxiv
This paper addresses the computational efficiency challenges in Graph Neural Networks by proposing a novel graph coarsening technique. Our approach significantly reduces inference time and memory while maintaining model performance, making GNNs more practical for real-world applications with large-scale graphs.
Graph Neural Networks Inference Optimization Graph Coarsening Performance

Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks

Shubhajit Roy, Shrutimoy Das, Binita Maity, Anant Kumar, Anirban Dasgupta • 2025

DiffCoAlg@Neurips
We propose a novel approach to enhance the expressivity of Graph Neural Networks beyond the traditional Weisfeiler-Leman hierarchy limitations. Our localization-based method improves the ability of GNNs to distinguish between different graph structures while maintaining computational efficiency.
Graph Neural Networks Expressivity Weisfeiler-Leman Localization

Research Interests

My research focuses on advancing the theoretical understanding and practical applications of Graph Neural Networks. I am particularly interested in:

  • Expressivity Enhancement: Developing methods to improve GNN expressivity beyond current theoretical limitations
  • Inference Optimization: Creating efficient algorithms for faster GNN inference in large-scale applications
  • Streaming Graph Learning: Addressing challenges in dynamic graph scenarios and catastrophic forgetting
  • Applications: Weather prediction, social network analysis, and knowledge graph construction