Search Results for author: Sayan Ranu

Found 30 papers, 14 papers with code

Learning Heuristics over Large Graphs via Deep Reinforcement Learning

2 code implementations NeurIPS 2020 Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, Ambuj Singh

Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality.

Combinatorial Optimization Q-Learning +2

GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation

1 code implementation22 Jan 2020 Nikhil Goyal, Harsh Vardhan Jain, Sayan Ranu

Minimum DFS codes are canonical labels and capture the graph structure precisely along with the label information.

Graph Generation

GraphReach: Position-Aware Graph Neural Network using Reachability Estimations

1 code implementation19 Aug 2020 Sunil Nishad, Shubhangi Agarwal, Arnab Bhattacharya, Sayan Ranu

In this paper, we develop GraphReach, a position-aware inductive GNN that captures the global positions of nodes through reachability estimations with respect to a set of anchor nodes.

Link Prediction Node Classification +1

Momentum Conserving Lagrangian Neural Networks

no code implementations29 Sep 2021 Ravinder Bhattoo, Sayan Ranu, N M Anoop Krishnan

However, these models still suffer from issues such as inability to generalize to arbitrary system sizes, poor interpretability, and most importantly, inability to learn translational and rotational symmetries, which lead to the conservation laws of linear and angular momentum, respectively.

Inductive Bias

Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias

no code implementations7 Oct 2021 Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan

However, these models still suffer from issues such as inability to generalize to arbitrary system sizes, poor interpretability, and most importantly, inability to learn translational and rotational symmetries, which lead to the conservation laws of linear and angular momentum, respectively.

Inductive Bias

NeuroMLR: Robust & Reliable Route Recommendation on Road Networks

1 code implementation NeurIPS 2021 Jayant Jain, Vrittika Bagadia, Sahil Manchanda, Sayan Ranu

First, our study reveals that a significant portion of the routes recommended by existing methods fail to reach the destination.

Task and Model Agnostic Adversarial Attack on Graph Neural Networks

1 code implementation25 Dec 2021 Kartik Sharma, Samidha Verma, Sourav Medya, Arnab Bhattacharya, Sayan Ranu

In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown.

Adversarial Attack Q-Learning

Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers

1 code implementation7 May 2022 Ashish Nair, Rahul Yadav, Anjali Gupta, Abhijnan Chakraborty, Sayan Ranu, Amitabha Bagchi

With the increasing popularity of food delivery platforms, it has become pertinent to look into the working conditions of the 'gig' workers in these platforms, especially providing them fair wages, reasonable working hours, and transparency on work availability.

Lifelong Learning for Neural powered Mixed Integer Programming

no code implementations24 Aug 2022 Sahil Manchanda, Sayan Ranu

In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs.

Graph Attention Knowledge Distillation

Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks

no code implementations3 Sep 2022 Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan

Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics.

Inductive Bias

Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems

no code implementations22 Sep 2022 Suresh Bishnoi, Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan

Neural networks with physics based inductive biases such as Lagrangian neural networks (LNN), and Hamiltonian neural networks (HNN) learn the dynamics of physical systems by encoding strong inductive biases.

Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network

1 code implementation23 Sep 2022 Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan

Lagrangian and Hamiltonian neural networks (LNNs and HNNs, respectively) encode strong inductive biases that allow them to outperform other models of physical systems significantly.

Global Counterfactual Explainer for Graph Neural Networks

1 code implementation21 Oct 2022 Mert Kosan, Zexi Huang, Sourav Medya, Sayan Ranu, Ambuj Singh

One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph.

counterfactual Counterfactual Explanation +2

Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems

1 code implementation10 Nov 2022 Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, N M Anoop Krishnan, Sayan Ranu

We evaluate these models on spring, pendulum, gravitational, and 3D deformable solid systems to compare the performance in terms of rollout error, conserved quantities such as energy and momentum, and generalizability to unseen system sizes.

StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes

no code implementations29 Jan 2023 Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N. M. Anoop Krishnan

Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, materials discovery, and mechanics.

GSHOT: Few-shot Generative Modeling of Labeled Graphs

1 code implementation6 Jun 2023 Sahil Manchanda, Shubham Gupta, Sayan Ranu, Srikanta Bedathur

Despite their initial success, these techniques, like much of the existing deep generative methods, require a large number of training samples to learn a good model.

Drug Discovery Few-Shot Learning

Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity

1 code implementation7 Jun 2023 Samidha Verma, Burouj Armgaan, Sourav Medya, Sayan Ranu

Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design.

counterfactual Counterfactual Explanation +2

FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks

1 code implementation14 Jun 2023 Mridul Gupta, Hariprasad Kodamana, Sayan Ranu

In addition, FRIGATE is robust to frugal sensor deployment, changes in road network connectivity, and temporal irregularity in sensing.

Spatio-Temporal Forecasting

Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics

no code implementations20 Jun 2023 Suresh Bishnoi, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan

Here, we propose a framework, namely Brownian graph neural networks (BROGNET), combining stochastic differential equations (SDEs) and GNNs to learn Brownian dynamics directly from the trajectory.

Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks

no code implementations11 Jul 2023 Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu, N M Anoop Krishnan

Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory.

Symbolic Regression

EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations

no code implementations3 Oct 2023 Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M Smedskjaer, Sayan Ranu, N M Anoop Krishnan

In addition to our thorough evaluation and analysis on eight existing datasets based on the benchmarking literature, we release two new benchmark datasets, propose four new metrics, and three challenging tasks.

Atomic Forces Benchmarking +1

GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking

no code implementations3 Oct 2023 Mert Kosan, Samidha Verma, Burouj Armgaan, Khushbu Pahwa, Ambuj Singh, Sourav Medya, Sayan Ranu

Motivated by this need, we present a benchmarking study on perturbation-based explainability methods for GNNs, aiming to systematically evaluate and compare a wide range of explainability techniques.

Benchmarking counterfactual

NeuroCUT: A Neural Approach for Robust Graph Partitioning

no code implementations18 Oct 2023 Rishi Shah, Krishnanshu Jain, Sahil Manchanda, Sourav Medya, Sayan Ranu

Second, we decouple the parameter space and the partition count making NeuroCUT inductive to any unseen number of partition, which is provided at query time.

graph partitioning

EUGENE: Explainable Unsupervised Approximation of Graph Edit Distance

no code implementations8 Feb 2024 Aditya Bommakanti, Harshith Reddy Vonteri, Sayan Ranu, Panagiotis Karras

The need to identify graphs having small structural distance from a query arises in biology, chemistry, recommender systems, and social network analysis.

Recommendation Systems

GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks

no code implementations20 Feb 2024 Anuj Kumar Sirohi, Anjali Gupta, Sayan Ranu, Sandeep Kumar, Amitabha Bagchi

Extensive experimentation on real-world datasets showcases the efficacy of GRAPHGINI in making significant improvements in individual fairness compared to all currently available state-of-the-art methods while maintaining utility and group equality.

Fairness

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