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Hermitian matrices for clustering directed graphs: insights and applications
Graph clustering is a basic technique in machine learning, and has widespread applications in different domains.
Distributed Graph Clustering and Sparsification
Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
Distributed Graph Clustering by Load Balancing
In this paper we present a simple and distributed algorithm for graph clustering: for a wide class of graphs that are characterised by a strong cluster-structure, our algorithm finishes in a poly-logarithmic number of rounds, and recovers a partition of the graph close to an optimal partition.
Partitioning Well-Clustered Graphs: Spectral Clustering Works!
In this paper we study variants of the widely used spectral clustering that partitions a graph into k clusters by (1) embedding the vertices of a graph into a low-dimensional space using the bottom eigenvectors of the Laplacian matrix, and (2) grouping the embedded points into k clusters via k-means algorithms.
