Furthermore, the high dimensionality of the scRNA-seq data and the inevitable noise pose significant challenges for clustering tasks. To address these challenges, in this study, we introduce a novel ...

Hierarchical Graph clustering on sparse graphs using the Affinity Clustering algorithm of Bateni et al. (NIPS 2017) ...

Wang, C., Hao, C. and Guan, X. (2020) Hierarchical and Overlapping Social Circle Identification in Ego Networks Based on Link Clustering. Neurocomputing, 381, 322-335.

The approach used to identify clusters, inspired by graph clustering theory (Schaeffer, 2007), seems to offer a valuable tool for such investigations. However, developing a generic cluster ...

The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph ...

The resulting architecture, Cluster-wise Graph Transformer (Cluster-GT), which uses node clusters as tokens and employs our proposed N2C-Attn module, shows superior performance on various graph-level ...

In this work, using a recently developed deep learning technology, i.e., cluster graph attention network (CGANet), combined with a homemade comprehensive genetic algorithm (CGA) program, we searched ...

Learn how to manage overlapping clusters in data sets with effective strategies including algorithm selection and feature engineering for better data science outcomes.

So, what is hypergraph? In simple terms, a hypergraph is an extension of a graph that allows edges to connect more than two vertices.