21 citations · 23 across the 5 of their papers we have counts for
5 papers
Game-theoretic Counterfactual Explanation for Graph Neural Networks
Chirag Chhablani, Sarthak Jain, Akshay Channesh +2
Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in complex networks. However, their decision-making processes remain a black-box to users, maki…
VeriBug: An Attention-based Framework for Bug-Localization in Hardware Designs
Giuseppe Stracquadanio, Sourav Medya, Stefano Quer +1
In recent years, there has been an exponential growth in the size and complexity of System-on-Chip designs targeting different specialized applications. The cost of an undetected b…
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
Hao Tian, Sourav Medya, Wei Ye
Combinatorial Optimization (CO) problems over graphs appear routinely in many applications such as in optimizing traffic, viral marketing in social networks, and matching for job a…
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…
A Survey on Explainability of Graph Neural Networks
Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma +2
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, includ…