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In analysis of graphs, the graph clustering problem is one of the most important problems, which is to divide all vertices of a given graph into some groups ...
Graph Clustering Based on Optimization of a Macroscopic Structure of Clusters. Yuta Taniguchi and Daisuke Ikeda. Department of Informatics, Kyushu University.
This paper presents a graph clustering algorithm which, given a graph and the number of clusters, tries to find a set of clusters such that the distance between ...
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Graph clustering has become ubiquitous in the study of relational data sets. ... Graph clustering based on optimization of a macroscopic structure of clusters.
The research for this thesis was carried out at the Centre for Mathematics and Com- puter Science (CWI) in Amsterdam and partially funded by Stichting Physica.
Jun 18, 2024 · The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex ...
In this paper we propose a structure-based clustering technique that transforms a given graph into a specific double tree structure called multi-level ...
The primary objective of graph clustering is to group or partition the nodes in a graph into clusters or communities based on their structural properties or ...
In this survey we overview the definitions and methods for graph clustering, that is, finding sets of ''related'' vertices in graphs.
Missing: Macroscopic | Show results with:Macroscopic
In this paper, we design a novel attributed graph clustering with dual redundancy reduction (AGC-DRR), which can re- duce the redundant information in both ...