Y. Xue, D. Kudenko, M. Khosla (2023), Graph learning-based generation of abstractions for reinforcement learning, In Neural Computing and Applications.

Iyiola E Olatunji, Mandeep Rathee, Thorben Funke, M. Khosla (2023), Private Graph Extraction via Feature Explanations, In Proceedings on Privacy Enhancing Technologies 2023(2) p.59-78.

Thi Ngan Dong, Johanna Schrader, Stefanie Mucke, Megha Khosla (2022), A message passing framework with multiple data integration for miRNA-disease association prediction, In Scientific Reports Volume 12.

Iyiola E. Olatunji, Jens Rauch, Matthias Katzensteiner, Megha Khosla (2022), A review of anonymization for healthcare data, In Big Data.

Jurek Leonhardt, Koustav Rudra, Megha Khosla, Abhijit Anand, Avishek Anand (2022), Efficient Neural Ranking using Forward Indexes, In Proceedings of the ACM Web Conference 2022.

Thi Ngan Dong, Stefanie Mucke, Megha Khosla (2022), MuCoMiD: A Multitask graph Convolutional Learning Framework for miRNA-Disease Association Prediction, In IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 19 p.3081-3092.

Thorben Funke, Megha Khosla, Mandeep Rathee, Avishek Anand (2022), Zorro: Valid, sparse, and stable explanations in graph neural networks, In IEEE Transactions on Knowledge & Data Engineering.

Megha Khosla, Vinay Setty, Avishek Anand (2021), A Comparative Study for Unsupervised Network Representation Learning, In IEEE Transactions on Knowledge and Data Engineering Volume 33 p.1807-1818.

Thi Ngan Dong, Graham Brogden, Gisa Gerold, Megha Khosla (2021), A multitask transfer learning framework for the prediction of virus-human protein-protein interactions, In BMC Bioinformatics Volume 22.

Iyiola E Olatunji, Wolfgang Nejdl, M. Khosla (2021), Membership Inference Attack on Graph Neural Networks, In IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications.