69 citations · 175 across the 40 of their papers we have counts for
11 papers · 1 filter
SMART: A Surrogate Model for Predicting Application Runtime in Dragonfly Systems
Xin Wang, Pietro Lodi Rizzini, Sourav Medya +1
The Dragonfly network, with its high-radix and low-diameter structure, is a leading interconnect in high-performance computing. A major challenge is workload interference on shared…
Preference-driven Knowledge Distillation for Few-shot Node Classification
Xing Wei, Chunchun Chen, Rui Fan +3
Graph neural networks (GNNs) can efficiently process text-attributed graphs (TAGs) due to their message-passing mechanisms, but their training heavily relies on the human-annotated…
DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding
Zhu Wang, Homaira Huda Shomee, Sathya N. Ravi +1
In the field of design patent analysis, traditional tasks such as patent classification and patent image retrieval heavily depend on the image data. However, patent images -- typic…
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
Peyman Baghershahi, Gregoire Fournier, Pranav Nyati +1
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs (TAGs), which are common in domains such as citation…
PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs
Homaira Huda Shomee, Suman Kalyan Maity, Sourav Medya
Large language models (LLMs) have emerged as transformative approaches in several important fields. This paper aims for a paradigm shift for patent writing by leveraging LLMs to ov…
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
Gregoire Fournier, Sourav Medya
Graph neural networks (GNNs) have been widely used in various domains such as social networks, molecular biology, or recommendation systems. Concurrently, different explanations me…
Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting
Peyman Baghershahi, Sourav Medya
Prompt tuning has become a key mechanism for adapting pre-trained Graph Neural Networks (GNNs) to new downstream tasks. However, existing approaches are predominantly supervised, r…
RIDGECUT: Learning Graph Partitioning with Rings and Wedges
Qize Jiang, Angelo Zangari, Linsey Pang +7
Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances. However, effectively incorp…
Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach
Rochana Chaturvedi, Peyman Baghershahi, Sourav Medya +1
Temporal information extraction from unstructured text is essential for contextualizing events and deriving actionable insights, particularly in the medical domain. We address the…
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Weizhi Zhang, Liangwei Yang, Wooseong Yang +5
Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with…
Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects
Shishir Adhikari, Sourav Medya, Elena Zheleva
In causal inference, interference refers to the phenomenon in which the actions of peers in a network can influence an individual's outcome. Peer effect refers to the difference in…