Date of Submission

6-2025

Date of Award

6-13-2025

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science

Department

Electronics and Communication Sciences Unit (ECSU-Kolkata)

Supervisor

Das, Swagatam

Abstract (Summary of the Work)

This dissertation addresses the challenge of scaling Graph Transformers by proposing a subgraph-based strategy to reduce attention complexity. The proposed framework preserves representational power while making attention computation tractable for largescale graphs. The method begins by partitioning the input graph into K subgraphs using the METIS algorithm. Each subgraph is encoded using a combination of local structural features from a Graph Convolutional Network (GCN) and global positional cues from Laplacian Positional Embeddings (LPEs). These embeddings are fused via a trainable projection function to form subgraph tokens. A supergraph is constructed to model interactions among subgraphs, allowing attention to be applied over a K × K matrix instead of the full n × n space, thereby reducing complexity from O(n2) to O(K2). Finally, a component-aware prediction strategy maps subgraph-level predictions to individual nodes using learned weights and regularization. Empirical evaluations demonstrate that the framework delivers higher accuracy, improved convergence, and scalability across diverse benchmark datasets.

Control Number

CS2316

DOI

https://dspace.isical.ac.in/items/00cefd0e-383b-4258-a920-5690c1ad7f54

DSpace Identifier

http://hdl.handle.net/10263/7593

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