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.
1 code implementation • 22 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.
1 code implementation • 19 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.
no code implementations • 29 Sep 2021 • Rishabh Ranjan, Siddharth Grover, Sourav Medya, Venkatesan Chakaravarthy, Yogish Sabharwal, Sayan Ranu
Subgraph edit distance (SED) is one of the most expressive measures of subgraph similarity.
no code implementations • 29 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.
no code implementations • 7 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.
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.
2 code implementations • 24 Dec 2021 • Rishabh Ranjan, Siddharth Grover, Sourav Medya, Venkatesan Chakaravarthy, Yogish Sabharwal, Sayan Ranu
To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee.
1 code implementation • 25 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.
1 code implementation • 7 Mar 2022 • Shubham Gupta, Sahil Manchanda, Srikanta Bedathur, Sayan Ranu
There has been a recent surge in learning generative models for graphs.
1 code implementation • 7 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.
no code implementations • 24 Aug 2022 • Sahil Manchanda, Sayan Ranu
In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs.
no code implementations • 3 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.
no code implementations • 22 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.
1 code implementation • 23 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.
1 code implementation • 21 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.
1 code implementation • 10 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.
no code implementations • 29 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.
1 code implementation • 6 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.
no code implementations • 6 Jun 2023 • Shubham Gupta, Sahil Manchanda, Sayan Ranu, Srikanta Bedathur
In this work, we address these limitations through a novel GNN framework called GRAFENNE.
1 code implementation • 7 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.
1 code implementation • 14 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.
no code implementations • 20 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.
no code implementations • 11 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.
no code implementations • 3 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.
no code implementations • 3 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.
Ranked #1 on Formation Energy on GeTe
1 code implementation • 14 Oct 2023 • Mridul Gupta, Sahil Manchanda, Hariprasad Kodamana, Sayan Ranu
GNNs, like other deep learning models, are data and computation hungry.
no code implementations • 18 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.
no code implementations • 8 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.
no code implementations • 20 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.