Sayan's Publications

The documents listed below have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Core Profile :

A* rated papers: 52, A rated papers: 2, <=B rated papers: 3

2025

Mridul Gupta, Samyak Jain, Vansh Ramani, Hariprasad Kodamana and Sayan Ranu, "Bonsai: Gradient-free Graph Condensation for Node Classification", in ICLR, 2025. (AR=32%) [Code]

2024

Burouj Armgaan, Manthan Dalmia, Sourav Medya, Sayan Ranu, "GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules", NeurIPS, 2024. (AR=25.8%) [Code]
Aditya Bommakanti, Harshith Reddy Vonteri, Konstantinos Skitsas, Sayan Ranu, Davide Mottin, Panagiotis Karras, "FUGAL: Feature-fortified Unrestricted Graph Alignment", NeurIPS, 2024. (AR=25.8%) [Code]
Mert Kosan, Zexi Huang, Sourav Medya, Sayan Ranu, Ambuj Singh, "GCFExplainer: Global Counterfactual Explainer for Graph Neural Networks", in ACM Transactions on Intelligent Systems and Technology, 2024.[Code].
Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu, and N M Anoop Krishnan, Discovering symbolic laws directly from trajectories with hamiltonian graph neural networks, Machine Learning: Science and Technology, Volume 5, Number 3.
Rishi Rajesh Shah, Krishnanshu Jain, Sahil Manchanda, Sourav Medya and Sayan Ranu, "NeuroCut: A Neural Approach for Robust Graph Partitioning", in KDD, 2024. (AR=20%)[Code][Video Abstract]
Samidha Verma, Burouj Armgaan, Sourav Medya and Sayan Ranu, "InduCE: Inductive Counterfactual Explanations for Graph Neural Networks", in TMLR, 2024. [Code]
Vaibhav Bihani, Sajid Mannan, Utkarsh Pratiush, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M. Smedskjaer, Sayan Ranu and N. M. Anoop Krishnan, Egraffbench: Evaluation of equivariant graph neural network force fields for atomistic simulations, Digital Discovery, 2024.
Mridul Gupta, Sahil Manchanda, Hariprasad Kodamana and Sayan Ranu, "Mirage: Model-agnostic Graph Distillation for Graph Classification", in ICLR, 2024. (AR=31%) [Code]
Mert Kosan*, Samidha Verma*, Burouj Armgaan, Khushbu Pahwa, Ambuj Singh, Sourav Medya and Sayan Ranu, "GnnX-Bench: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking", in ICLR, 2024. (AR=31%) [Code]
Suresh Bishnoi, Jayadeva Jayadeva, Sayan Ranu and N.M. Anoop Krishnan"BroGNet: Momentum-Conserving Graph Neural Stochastic Differential Equation for Learning Brownian Dynamics", in ICLR, 2024. (AR=31%) [Code]

2023

Sahil Manchanda, Shubham Gupta, Sayan Ranu, Srikanta Bedathur, "Generative modeling of labeled graphs under data scarcity", in LOG, 2023. [Code]
Sachin Chauhan, Zeel Patel, Sayan Ranu, Rijurekha Sen, and Nipun Batra, "Fine-Grained Spatio-Temporal Particulate Matter Dataset From Delhi For ML based Modeling", in NeurIPS, 2023. (AR=32.7%) [Code]
Mridul Gupta, Hariprasad Kodamana, and Sayan Ranu, "Frigate: Frugal Spatio-temporal Forecasting on Road Networks", in KDD, 2023. (AR=22.1%) [Code]
Shubham Gupta*, Sahil Manchanda*, Sayan Ranu and Srikanta Bedathur, "GRAFENNE: Continual learning on Graphs with Heterogeneous and Dynamic Feature Sets", in ICML, 2023. (AR=27.9%) [Code]
Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu and N.M. Anoop Krishnan, StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes", in ICML, 2023. (AR=27.9%) [Code]
Anjali Gupta, Shreyans Nagori, Abhijnan Chakraborty, Rohit Vaish, Sayan Ranu, Prajit Nadkarni, Narendra Dasararaju and Muthusamy Chelliah, "Towards Fair Allocation in Social Commerce Platforms", in WWW, 2023. (AR=19.1%) [Code]
Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu and N.M. Anoop Krishnan, "Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems", in ICLR, 2023. (AR=31.8%) [Code]
Ravinder Bhattoo, Sayan Ranu and N. M. Anoop Krishnan, "Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks", in Machine Learning: Science and Technology, 2023. (Impact Factor 6.013). [Code]
Sahil Manchanda and Sayan Ranu, "LIMIP: Lifelong Learning to solve Mixed Integer Programs", in AAAI, 2023. (Oral Presentation, AR<=19.6%) [Code]
Kartik Sharma, Samidha Verma, Sourav Medya, Arnab Bhattacharya and Sayan Ranu, "Task and Model Agnostic Adversarial Attack on Graph Neural Networks", in AAAI, 2023. (Oral Presentation, AR<=19.6%) [Code]
Mert Kosan, Zexi Huang, Sourav Medya, Sayan Ranu, Ambuj Singh, "Global Counterfactual Explainer for Graph Neural Networks", in WSDM, 2023. (Oral Presentation, AR=7.5%)[Code][Top-10 papers of WSDM 2023]
Pritish Chakraborty, Sayan Ranu, Ipsit Mantri, Abir de, "Learning and Maximizing Influence in Social Networks Under Capacity Constraints", in WSDM, 2023. (AR=17.8%)
Mahdihusain Momin, Raj Kamal, Shantwana Dixit, Sayan Ranu and Amitabha Bagchi, "KWIQ: Answering k-core Window Queries in Temporal Networks", in EDBT, 2023. [Code]

2022

Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, N M Anoop Krishnan, and Sayan Ranu, "Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems", in NeurIPS, 2022. [Code]
Rishabh Ranjan, Siddharth Grover, Sourav Medya, Venkatesan Chakaravarthy, Yogish Sabharwal and Sayan Ranu, "GREED: A Neural Framework for Learning Graph Distance Functions", in NeurIPS, 2022. (AR=25.6%)[Code]
Ravinder Bhattoo, Sayan Ranu and Anoop Krishnan, "Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network", in NeurIPS, 2022. (AR=25.6%, Spotlight) [Code]
Ashish Nair, Rahul Yadav, Anjali Gupta, Abhijnan Chakraborty, Sayan Ranu and Amitabha Bagchi, "Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers", in IJCAI, 2022. (AR=16%) [Code]
Anjali Gupta, Rahul Yadav, Ashish Nair, Abhijnan Chakraborty, Sayan Ranu and Amitabha Bagchi, "FairFoody: Bringing in Fairness in Food Delivery", in AAAI, 2022. (AR=4%, Oral Presentation) [Code]
Shubham Gupta, Sahil Manchanda, Srikanta Bedathur and Sayan Ranu, "TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs", in AAAI, 2022. (AR=4%, Oral Presentation) [Code]
Komal Chauhan, Kartik Jain, Sayan Ranu, Srikanta Bedathur and Amitabha Bagchi, "Answering Regular Path Queries through Exemplars", in VLDB, 2022. [Code]
Manas Joshi, Arshdeep Singh, Sayan Ranu, Amitabha Bagchi, Priyank Karia, and Puneet Kala, "FoodMatch: Batching and Matching for Food Delivery in Dynamic Road Networks", in ACM Transactions on Spatial Algorithms and Systems, 2022. (Impact Factor: 2.304) [Data][Code]

2017

Aravind Sankar, Sayan Ranu and Karthik Raman, "Predicting Novel Metabolic Pathways through Subgraph Mining" in Bioinformatics, 2017. (Impact Factor: 7.3) [Media Coverage][Code]
Shiladitya Pande, Sayan Ranu and Arnab Bhattacharya,"SkyGraph: Retrieving Regions of Interest using Skyline Subgraph Queries", in VLDB, 2017. (AR=18.6%)[Code]
Akhil Arora, Sainyam Galhotra and Sayan Ranu,"Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study", in SIGMOD, 2017. [Project Page] [Code](AR=19%) [SIGMOD 2017 Most Reproducible Paper award]
Note: It has come to our notice that groups in University of British Columbia (headed by Lakshmanan et al.) and Nanyang Technical University (Xiao et al.), have published refutations on our benchmarking study as a technical report. Read our point-by-point response to the refutations.
Additionally, we have the following comments to make:
- Our paper has been independently verified by SIGMOD and has received the "SIGMOD Reproducibility Award".
- Due to an undocumented assumption in the SIMPATH code released by the authors of SIMPATH paper, the running times reported for SIMPATH on the DBLP and YouTube datasets in our paper are not a true reflection of the SIMPATH algorithm. The correct running times are reported in the point-by-point response. The corrected running times however, do not cause any change in the conclusions drawn.

Soundhararajan Gopi, Animesh Singh, Swaathiratna Suresh, Suvadip Paul, Sayan Ranu and Athi N Naganathan,"Toward a Quantitative Description of Microscopic Pathway Heterogeneity in Protein Folding", in Physical Chemistry Chemical Physics, 2017. (Impact Factor: 4.123)
Shubhadip Mitra, Priya Saraf, Richa Sharma, Arnab Bhattacharya, Sayan Ranu, and Harsh Bhandari, "NetClus: A Scalable Framework for Locating Top-k Sites for Placement of Trajectory-Aware Services", in ICDE, 2017. (Short Paper)