69 citations · 294 across the 28 of their papers we have counts for
10 papers · 1 filter
Global Counterfactual Explainer for Graph Neural Networks
Mert Kosan, Zexi Huang, Sourav Medya +2
Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, ther…
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi +3
Recently, graph neural networks have been gaining a lot of attention to simulate dynamical systems due to their inductive nature leading to zero-shot generalizability. Similarly, p…
Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks
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. However, traditional approaches require the kn…
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
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…
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
Suresh Bishnoi, Ravinder Bhattoo, Sayan Ranu +1
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 encodi…
Lifelong Learning for Neural powered Mixed Integer Programming
Sahil Manchanda, Sayan Ranu
Mixed Integer programs (MIPs) are typically solved by the Branch-and-Bound algorithm. Recently, Learning to imitate fast approximations of the expert strong branching heuristic has…
Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers
Ashish Nair, Rahul Yadav, Anjali Gupta +3
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…
Unsupervised Graph Neural Network Reveals the Structure--Dynamics Correlation in Disordered Systems
Vaibhav Bihani, Sahil Manchanda, Sayan Ranu +1
Learning the structure--dynamics correlation in disordered systems is a long-standing problem. Here, we use unsupervised machine learning employing graph neural networks (GNN) to i…
FairFoody: Bringing in Fairness in Food Delivery
Anjali Gupta, Rahul Yadav, Ashish Nair +3
Along with the rapid growth and rise to prominence of food delivery platforms, concerns have also risen about the terms of employment of the gig workers underpinning this growth. O…
TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs
Shubham Gupta, Sahil Manchanda, Srikanta Bedathur +1
There has been a recent surge in learning generative models for graphs. While impressive progress has been made on static graphs, work on generative modeling of temporal graphs is…