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Found 5 papers, 5 papers with code
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability.
Asymmetric Semi-Nonnegative Matrix Factorization for Directed Graph Clustering
Graph clustering is a fundamental task in the network analysis, which is essential for many modern applications.
Self-Paced Multi-Label Learning with Diversity
The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard.
Dynamic Graph-Based Label Propagation for Density Peaks Clustering
The cut-off distance affects the local density values and is calculated in different ways depending on the size of the datasets, which can influence the quality of clustering.
An Improved Density Peaks Method for Data Clustering
In the second step, a novel label propagation method is proposed to form clusters.
