5 papers · 1 filter
Hyperbolic Gaussian Blurring Mean Shift: A Statistical Mode-Seeking Framework for Clustering in Curved Spaces
Arghya Pratihar, Arnab Seal, Swagatam Das +1
Clustering is a fundamental unsupervised learning task for uncovering patterns in data. While Gaussian Blurring Mean Shift (GBMS) has proven effective for identifying arbitrarily s…
Learning from Heterophilic Graphs: A Spectral Theory Perspective on the Impact of Self-Loops and Parallel Edges
Kushal Bose, Swagatam Das
Graph heterophily poses a formidable challenge to the performance of Message-passing Graph Neural Networks (MP-GNNs). The familiar low-pass filters like Graph Convolutional Network…
Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks
Kushal Bose, Swagatam Das
Graph Neural Networks (GNNs) suffer from oversquashing, where structural bottlenecks limit message propagation between distant nodes, hindering tasks that require long-range intera…
Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration
Arghya Pratihar, Kushal Bose, Swagatam Das
Clustering aims at partitioning data points into groups of similar objects without knowing about the class labels. However, clustering datasets with complex geometric structures, s…
Hyperbolic Fuzzy C-Means with Adaptive Weight-based Filtering for Efficient Clustering
Swagato Das, Arghya Pratihar, Swagatam Das
Clustering algorithms play a pivotal role in unsupervised learning by identifying and grouping similar objects based on shared characteristics. Although traditional clustering tech…