4 citations · 9 across the 16 of their papers we have counts for
15 papers · 1 filter
Lethe: Link Inference Attacks For Evaluation of Edge Unlearning Methods
Andrea D'Angelo, Francesco Gullo, Davide Mottin +2
Graph Unlearning (GU) aims to remove the influence of specific training data from a Graph Neural Network without retraining anew. While most methods address removal of nodes and fe…
DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction
Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal +2
High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with thermo…
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
Parth Verma, Parv P. Singh, Vipul Garg +3
Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new ch…
Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
Mridul Gupta, Samyak Jain, Vansh Ramani +2
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their scalability is increasingly strained by the size of real-world graphs in domains…
GnnXemplar: Exemplars to Explanations -- Natural Language Rules for Global GNN Interpretability
Burouj Armgaan, Eshan Jain, Harsh Pandey +2
Graph Neural Networks (GNNs) are widely used for node classification, yet their opaque decision-making limits trust and adoption. While local explanations offer insights into indiv…
Panorama: Fast-Track Nearest Neighbors
Vansh Ramani, Alexis Schlomer, Akash Nayar +3
Approximate Nearest-Neighbor Search (ANNS) pipelines for high-dimensional neural embeddings spend the bulk of their query time in candidate verification, making it the primary bott…
GRAIL: Graph Edit Distance and Node Alignment Using LLM-Generated Code
Samidha Verma, Arushi Goyal, Ananya Mathur +2
Graph Edit Distance (GED) is a widely used metric for measuring similarity between two graphs. Computing the optimal GED is NP-hard, leading to the development of various neural an…
GNNMerge: Merging of GNN Models Without Accessing Training Data
Vipul Garg, Ishita Thakre, Sayan Ranu
Model merging has gained prominence in machine learning as a method to integrate multiple trained models into a single model without accessing the original training data. While exi…
Bonsai: Gradient-free Graph Condensation for Node Classification
Mridul Gupta, Samyak Jain, Vansh Ramani +2
Graph condensation has emerged as a promising avenue to enable scalable training of GNNs by compressing the training dataset while preserving essential graph characteristics. Our s…
GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
Anuj Kumar Sirohi, Anjali Gupta, Sandeep Kumar +2
Graph Neural Networks (GNNs) have demonstrated impressive performance across various tasks, leading to their increased adoption in high-stakes decision-making systems. However, con…
EUGENE: Explainable Structure-aware Graph Edit Distance Estimation with Generalized Edit Costs
Aditya Bommakanti, Harshith Reddy Vonteri, Sayan Ranu +1
The need to identify graphs with small structural distances from a query arises in domains such as biology, chemistry, recommender systems, and social network analysis. Among sever…
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan +7
Equivariant graph neural networks force fields (EGraFFs) have shown great promise in modelling complex interactions in atomic systems by exploiting the graphs' inherent symmetries.…
NeuroCUT: A Neural Approach for Robust Graph Partitioning
Rishi Shah, Krishnanshu Jain, Sahil Manchanda +2
Graph partitioning aims to divide a graph into disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhib…
Mirage: Model-Agnostic Graph Distillation for Graph Classification
Mridul Gupta, Sahil Manchanda, Hariprasad Kodamana +1
GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource e…
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
Mert Kosan, Samidha Verma, Burouj Armgaan +4
Numerous explainability methods have been proposed to shed light on the inner workings of GNNs. Despite the inclusion of empirical evaluations in all the proposed algorithms, the i…