96 citations · 96 across the 6 of their papers we have counts for
7 papers
Transformers Can Learn Connectivity in Some Graphs but Not Others
Amit Roy, Abulhair Saparov
Reasoning capability is essential to ensure the factual correctness of the responses of transformer-based Large Language Models (LLMs), and robust reasoning about transitive relati…
LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs
Amit Roy, Ning Yan, Masood Mortazavi
Dynamic Text-Attributed Graphs (DyTAGs) have numerous real-world applications, e.g. social, collaboration, citation, communication, and review networks. In these networks, nodes an…
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Amit Roy, Juan Shu, Jia Li +4
Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection,…
Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network
Amit Roy, Kashob Kumar Roy, Amin Ahsan Ali +2
Research in deep learning models to forecast traffic intensities has gained great attention in recent years due to their capability to capture the complex spatio-temporal relations…
Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation
Kashob Kumar Roy, Amit Roy, A K M Mahbubur Rahman +2
Graph Neural Networks (GNNs) learn low dimensional representations of nodes by aggregating information from their neighborhood in graphs. However, traditional GNNs suffer from two…
Structure-Aware Hierarchical Graph Pooling using Information Bottleneck
Kashob Kumar Roy, Amit Roy, A K M Mahbubur Rahman +2
Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in graph classification and regression tasks. For these tasks, different pooling strategies have been propo…
SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network
Amit Roy, Kashob Kumar Roy, Amin Ahsan Ali +2
To capture spatial relationships and temporal dynamics in traffic data, spatio-temporal models for traffic forecasting have drawn significant attention in recent years. Most of the…