69 citations · 294 across the 28 of their papers we have counts for
18 papers · 1 filter
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
Suresh Bishnoi, Ravinder Bhattoo, Jayadeva +2
The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these quantities are derive…
Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics
Suresh Bishnoi, Jayadeva, Sayan Ranu +1
Neural networks (NNs) that exploit strong inductive biases based on physical laws and symmetries have shown remarkable success in learning the dynamics of physical systems directly…
FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks
Mridul Gupta, Hariprasad Kodamana, Sayan Ranu
Modelling spatio-temporal processes on road networks is a task of growing importance. While significant progress has been made on developing spatio-temporal graph neural networks (…
Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity
Samidha Verma, Burouj Armgaan, Sourav Medya +1
Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design. However, GNNs lack transparency as they cannot pr…
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Shubham Gupta, Sahil Manchanda, Sayan Ranu +1
Graph neural networks (GNNs), in general, are built on the assumption of a static set of features characterizing each node in a graph. This assumption is often violated in practice…
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Sahil Manchanda, Shubham Gupta, Sayan Ranu +1
Deep graph generative modeling has gained enormous attraction in recent years due to its impressive ability to directly learn the underlying hidden graph distribution. Despite thei…
StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry +2
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, ma…
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…
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…
Task and Model Agnostic Adversarial Attack on Graph Neural Networks
Kartik Sharma, Samidha Verma, Sourav Medya +2
Adversarial attacks on Graph Neural Networks (GNNs) reveal their security vulnerabilities, limiting their adoption in safety-critical applications. However, existing attack strateg…
GREED: A Neural Framework for Learning Graph Distance Functions
Rishabh Ranjan, Siddharth Grover, Sourav Medya +3
Among various distance functions for graphs, graph and subgraph edit distances (GED and SED respectively) are two of the most popular and expressive measures. Unfortunately, exact…
Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias
Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
Realistic models of physical world rely on differentiable symmetries that, in turn, correspond to conservation laws. Recent works on Lagrangian and Hamiltonian neural networks show…
GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation
Nikhil Goyal, Harsh Vardhan Jain, Sayan Ranu
Graph generative models have been extensively studied in the data mining literature. While traditional techniques are based on generating structures that adhere to a pre-decided di…
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
Sahil Manchanda, Akash Mittal, Anuj Dhawan +3
There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused on ob…