47 citations · 54 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
SCNode: Spatial and Contextual Coordinates for Graph Representation Learning
Md Joshem Uddin, Astrit Tola, Varin Sikand +2
Effective node representation lies at the heart of Graph Neural Networks (GNNs), as it directly impacts their ability to perform downstream tasks such as node classification and li…
TopER: Topological Embeddings in Graph Representation Learning
Astrit Tola, Funmilola Mary Taiwo, Cuneyt Gurcan Akcora +1
Graph embeddings play a critical role in graph representation learning, allowing machine learning models to explore and interpret graph-structured data. However, existing methods o…
Topological Methods in Machine Learning: A Tutorial for Practitioners
Baris Coskunuzer, Cüneyt Gürcan Akçora
Topological Machine Learning (TML) is an emerging field that leverages techniques from algebraic topology to analyze complex data structures in ways that traditional machine learni…
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT
Cuneyt Gurcan Akcora, Murat Kantarcioglu, Yulia R. Gel +1
Topological data analysis (TDA) delivers invaluable and complementary information on the intrinsic properties of data inaccessible to conventional methods. However, high computatio…
Smart Vectorizations for Single and Multiparameter Persistence
Baris Coskunuzer, CUneyt Gurcan Akcora, Ignacio Segovia Dominguez +3
The machinery of topological data analysis becomes increasingly popular in a broad range of machine learning tasks, ranging from anomaly detection and manifold learning to graph cl…