6 papers
HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care
Thummaluru Siddartha Reddy, Vempalli Naga Sai Saketh, Yash Punjabi +1
Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in health…
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
Ayushman Raghuvanshi, Thummaluru Siddartha Reddy, Sundeep Prabhakar Chepuri +1
Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key…
Self-Adaptive Graph Mixture of Models
Mohit Meena, Yash Punjabi, Abhishek A +2
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over graph-structured data, yet recent studies have shown that their performance gains are beginning to pla…
PLGC: Pseudo-Labeled Graph Condensation
Jay Nandy, Arnab Kumar Mondal, Anuj Rathore +1
Large graph datasets make training graph neural networks (GNNs) computationally costly. Graph condensation methods address this by generating small synthetic graphs that approximat…
Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments
Harsh Vishwakarma, Ankush Agarwal, Ojas Patil +2
Enterprise systems are crucial for enhancing productivity and decision-making among employees and customers. Integrating LLM based systems into enterprise systems enables intellige…
GnnXemplar: Exemplars to Explanations -- Natural Language Rules for Global GNN Interpretability
Burouj Armgaan, Eshan Jain, Harsh Pandey +2
Graph Neural Networks (GNNs) are widely used for node classification, yet their opaque decision-making limits trust and adoption. While local explanations offer insights into indiv…