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Samidha Verma

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4 papers

2 author rows

ICML Conference 2025 Conference Paper

GRAIL: Graph Edit Distance and Node Alignment using LLM-Generated Code

Samidha Verma
Arushi Goyal
Ananya Mathur
Ankit Anand
Sayan Ranu

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 and non-neural heuristics. While neural methods have achieved improved approximation quality compared to non-neural approaches, they face significant challenges: (1) They require large amounts of ground truth data, which is itself NP-hard to compute. (2) They operate as black boxes, offering limited interpretability. (3) They lack cross-domain generalization, necessitating expensive retraining for each new dataset. We address these limitations with GRAIL, introducing a paradigm shift in this domain. Instead of training a neural model to predict GED, GRAIL employs a novel combination of large language models (LLMs) and automated prompt tuning to generate a program that is used to compute GED. This shift from predicting GED to generating programs imparts various advantages, including end-to-end interpretability and an autonomous self-evolutionary learning mechanism without ground-truth supervision. Extensive experiments on seven datasets confirm that GRAIL not only surpasses state-of-the-art GED approximation methods in prediction quality but also achieves robust cross-domain generalization across diverse graph distributions.

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ICLR Conference 2024 Conference Paper

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

Mert Kosan
Samidha Verma
Burouj Armgaan
Khushbu Pahwa
Ambuj K. Singh
Sourav Medya
Sayan Ranu

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 interrogative aspects of these evaluations lack diversity. As a result, various facets of explainability pertaining to GNNs, such as a comparative analysis of counterfactual reasoners, their stability to variational factors such as different GNN architectures, noise, stochasticity in non-convex loss surfaces, feasibility amidst domain constraints, and so forth, have yet to be formally investigated. 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. Among the key findings of our study, we identify the Pareto-optimal methods that exhibit superior efficacy and stability in the presence of noise. Nonetheless, our study reveals that all algorithms are affected by stability issues when faced with noisy data. Furthermore, we have established that the current generation of counterfactual explainers often fails to provide feasible recourses due to violations of topological constraints encoded by domain-specific considerations. Overall, this benchmarking study empowers stakeholders in the field of GNNs with a comprehensive understanding of the state-of-the-art explainability methods, potential research problems for further enhancement, and the implications of their application in real-world scenarios.

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TMLR Journal 2024 Journal Article

InduCE: Inductive Counterfactual Explanations for Graph Neural Networks

Samidha Verma
Burouj Armgaan
Sourav Medya
Sayan Ranu

Graph neural networks (GNNs) drive several real-world applications including drug-discovery, recommendation engines, and chip designing. Unfortunately, GNNs are a black-box since they do not allow human-intelligible explanations of their predictions. Counterfactual reasoning is an effort to overcome this limitation. Specifically, the objective is to minimally perturb the input graph to a GNN, so that its prediction changes. While several algorithms have been proposed towards counterfactual explanations of GNNs, majority suffer from three key limitations: (1) they only consider perturbations in the form of deletions of existing edges, (2) they perform an inefficient exploration of the combinatorial search space, (3) the counterfactual explanation model is transductive in nature, i.e., they do not generalize to unseen data. In this work, we propose an inductive algorithm called InduCE, that overcomes these limitations. Through extensive experiments on graph datasets, we show that incorporating edge additions, and modelling marginal effect of perturbations aid in generating better counterfactuals among available recourse. Furthermore, inductive modeling enables InduCE to directly predict counterfactual perturbations without requiring instance-specific training. This leads to significant computational speed-up over baselines and allows counterfactual analyses for GNNs at scale.

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AAAI Conference 2023 Conference Paper

Task and Model Agnostic Adversarial Attack on Graph Neural Networks

Kartik Sharma
Samidha Verma
Sourav Medya
Arnab Bhattacharya
Sayan Ranu

Adversarial attacks on Graph Neural Networks (GNNs) reveal their security vulnerabilities, limiting their adoption in safety-critical applications. However, existing attack strategies rely on the knowledge of either the GNN model being used or the predictive task being attacked. Is this knowledge necessary? For example, a graph may be used for multiple downstream tasks unknown to a practical attacker. It is thus important to test the vulnerability of GNNs to adversarial perturbations in a model and task-agnostic setting. In this work, we study this problem and show that Gnns remain vulnerable even when the downstream task and model are unknown. The proposed algorithm, TANDIS (Targeted Attack via Neighborhood DIStortion) shows that distortion of node neighborhoods is effective in drastically compromising prediction performance. Although neighborhood distortion is an NP-hard problem, TANDIS designs an effective heuristic through a novel combination of Graph Isomorphism Network with deep Q-learning. Extensive experiments on real datasets show that, on average, TANDIS is up to 50% more effective than state-of-the-art techniques, while being more than 1000 times faster.

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