17 citations · 22 across the 9 of their papers we have counts for
10 papers
Inductive Correlation Clustering with Graph Neural Networks
Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan +1
Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specifie…
ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
Yu Zhang, Sean Bin Yang, Arijit Khan +1
Counterfactual explanations offer an intuitive way to interpret graph neural networks (GNNs) by identifying minimal changes that alter a model's prediction, thereby answering "what…
In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration
Jiajie Fu, Haitong Tang, Arijit Khan +3
Entity Resolution (ER) is a fundamental data quality improvement task that identifies and links records referring to the same real-world entity. Traditional ER approaches often rel…
Finding Counterfactual Evidences for Node Classification
Dazhuo Qiu, Jinwen Chen, Arijit Khan +2
Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and i…
SliceGX: Layer-wise GNN Explanation with Model-slicing
Tingting Zhu, Tingyang Chen, Yinghui Wu +2
Ensuring the trustworthiness of graph neural networks (GNNs), which are often treated as black-box models, requires effective explanation techniques. Existing GNN explanations typi…
Interpreting Graph Inference with Skyline Explanations
Dazhuo Qiu, Haolai Che, Arijit Khan +1
Inference queries have been routinely issued to graph machine learning models such as graph neural networks (GNNs) for various network analytical tasks. Nevertheless, GNN outputs a…
Graph Data Management and Graph Machine Learning: Synergies and Opportunities
Arijit Khan, Xiangyu Ke, Yinghui Wu
The ubiquity of machine learning, particularly deep learning, applied to graphs is evident in applications ranging from cheminformatics (drug discovery) and bioinformatics (protein…
Generating Robust Counterfactual Witnesses for Graph Neural Networks
Dazhuo Qiu, Mengying Wang, Arijit Khan +1
This paper introduces a new class of explanation structures, called robust counterfactual witnesses (RCWs), to provide robust, both counterfactual and factual explanations for grap…
Machine Learning for Blockchain Data Analysis: Progress and Opportunities
Poupak Azad, Cuneyt Gurcan Akcora, Arijit Khan
Blockchain technology has rapidly emerged to mainstream attention, while its publicly accessible, heterogeneous, massive-volume, and temporal data are reminiscent of the complex dy…
View-based Explanations for Graph Neural Networks
Tingyang Chen, Dazhuo Qiu, Yinghui Wu +3
Generating explanations for graph neural networks (GNNs) has been studied to understand their behavior in analytical tasks such as graph classification. Existing approaches aim to…