We give algorithms for finding graph clusters and drawing graphs, highlighting local community structure within the context of a larger network. For a given graph G, we use the per...
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are...
Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Ray...
A novel breadth-first based structural clustering method for graphs is proposed. Clustering is an important task for analyzing complex networks such as biological networks, World ...
We consider clustering situations in which the pairwise affinity between data points depends on a latent ”context” variable. For example, when clustering features arising fro...
We propose a hybrid clustering strategy by integrating heterogeneous information sources as graphs. The hybrid clustering method is extended on the basis of modularity based Louva...
Xinhai Liu, Shi Yu, Yves Moreau, Frizo A. L. Janss...