Sourav Medya

Orcid: 0000-0003-0996-2807

According to our database1, Sourav Medya authored at least 64 papers between 2016 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2025
DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding.
CoRR, August, 2025

From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context.
CoRR, August, 2025

PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs.
CoRR, July, 2025

Unsupervised Prompting for Graph Neural Networks.
CoRR, May, 2025

COMRECGC: Global Graph Counterfactual Explainer through Common Recourse.
CoRR, May, 2025

LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation.
CoRR, March, 2025

Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects.
CoRR, March, 2025

Fact-based Counter Narrative Generation to Combat Hate Speech.
Proceedings of the ACM on Web Conference 2025, 2025

BANGS: Game-theoretic Node Selection for Graph Self-Training.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

How Older Adults Communicate their Technology Problems: Challenges and Design Opportunities.
Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2025

A Survey on Patent Analysis: From NLP to Multimodal AI.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024
InduCE: Inductive Counterfactual Explanations for Graph Neural Networks.
Trans. Mach. Learn. Res., 2024

Uncertainty in Graph Neural Networks: A Survey.
Trans. Mach. Learn. Res., 2024

Design Requirements for Human-Centered Graph Neural Network Explanations.
CoRR, 2024

A Comprehensive Survey on AI-based Methods for Patents.
CoRR, 2024

Game-theoretic Counterfactual Explanation for Graph Neural Networks.
Proceedings of the ACM on Web Conference 2024, 2024

Incorporating Heterophily into Graph Neural Networks for Graph Classification.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2024

IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

NeuroCut: A Neural Approach for Robust Graph Partitioning.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

DETECTive: Machine Learning-driven Automatic Test Pattern Prediction for Faults in Digital Circuits.
Proceedings of the Great Lakes Symposium on VLSI 2024, 2024

An Experimental Analysis on Evaluating Patent Citations.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Predictive Modeling of Application Runtime in Dragonfly Systems.
Proceedings of the Dynamic Data Driven Applications Systems - 5th International Conference, 2024

VeriBug: An Attention-Based Framework for Bug Localization in Hardware Designs.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2024

COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
A Survey on Explainability of Graph Neural Networks.
IEEE Data Eng. Bull., 2023

Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity.
CoRR, 2023

Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis.
CoRR, 2023

Global Counterfactual Explainer for Graph Neural Networks.
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Feature-based Individual Fairness in k-clustering.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Task and Model Agnostic Adversarial Attack on Graph Neural Networks.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Approximate Algorithms for Data-Driven Influence Limitation.
IEEE Trans. Knowl. Data Eng., 2022

Incorporating Heterophily into Graph Neural Networks for Graph Classification.
CoRR, 2022

An Exploratory Study of Stock Price Movements from Earnings Calls.
Proceedings of the Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25, 2022

GREED: A Neural Framework for Learning Graph Distance Functions.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
MetaLearning with Graph Neural Networks: Methods and Applications.
SIGKDD Explor., 2021

A Neural Framework for Learning Subgraph and Graph Similarity Measures.
CoRR, 2021

Event Detection on Dynamic Graphs.
CoRR, 2021

Feature-based Individual Fairness in k-Clustering.
CoRR, 2021

Meta-Learning with Graph Neural Networks: Methods and Applications.
CoRR, 2021

Balance Maximization in Signed Networks via Edge Deletions.
Proceedings of the WSDM '21, 2021

Network Robustness via Global k-cores.
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020
Investigating the Ground-level Ozone Formation and Future Trend in Taiwan.
CoRR, 2020

GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Game Theoretic Approach For Core Resilience.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

A Game Theoretic Approach For k-Core Minimization.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Manipulating Node Similarity Measures in Networks.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019
Scalable Algorithms for Network Design.
PhD thesis, 2019

Manipulating Node Similarity Measures in Network.
CoRR, 2019

Learning Heuristics over Large Graphs via Deep Reinforcement Learning.
CoRR, 2019

K-Core Minimization: A Game Theoretic Approach.
CoRR, 2019

Influence Minimization Under Budget and Matroid Constraints: Extended Version.
CoRR, 2019

Covert Networks: How Hard is It to Hide?
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018
Making a Small World Smaller: Path Optimization in Networks.
IEEE Trans. Knowl. Data Eng., 2018

Noticeable Network Delay Minimization via Node Upgrades.
Proc. VLDB Endow., 2018

Group Centrality Maximization via Network Design.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

2017
Maximizing Coverage Centrality via Network Design: Extended Version.
CoRR, 2017

Predictive modeling and scalability analysis for large graph analytics.
Proceedings of the 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), 2017

2016
Towards Performance and Scalability Analysis of Distributed Memory Programs on Large-Scale Clusters.
Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering, 2016

Towards Scalable Network Delay Minimization.
Proceedings of the IEEE 16th International Conference on Data Mining, 2016


  Loading...