scholarly journals Revealing Consensus and Dissensus between Network Partitions

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Tiago P. Peixoto
Keyword(s):  
Network Partitions

Designing Network Partitions to Improve Maintenance Routing

1997 ◽  
Vol 3 (4) ◽  
pp. 160-168 ◽  
Author(s):  
Padma Kandula ◽  
Jeff R. Wright
Keyword(s):  
Network Partitions

Cluster Validity Measures Based Agglomerative Hierarchical Clustering for Network Data

2019 ◽  
Vol 23 (3) ◽  
pp. 577-583
Author(s):  
Yukihiro Hamasuna ◽  
Shusuke Nakano ◽  
Ryo Ozaki ◽  
and Yasunori Endo ◽  
◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Optimal Number ◽  
Network Data ◽  
Network Clustering ◽  
Agglomerative Hierarchical Clustering ◽  
Cluster Validity ◽  
Validity Measures ◽  
Evaluation Measure ◽  
Network Partitions ◽  
Optimal Number Of Clusters

The Louvain method is a method of agglomerative hierarchical clustering (AHC) that uses modularity as the merging criterion. Modularity is an evaluation measure for network partitions. Cluster validity measures are also used to evaluate cluster partitions and to determine the optimal number of clusters. Several cluster validity measures are constructed considering the geometric features of clusters. These measures and modularity are considered to be the same concept in the viewpoint of evaluating cluster partitions. In this paper, cluster validity measures based agglomerative hierarchical clustering (CVAHC) is proposed as a novel clustering method for network data. The cluster validity measures are used as a merging criterion and an evaluation measure for network data in the proposed method. Numerical experiments show that Dunn’s and Xie-Beni’s indices for network partitions are useful for network clustering.

Cluster Validity Measures for Network Data

2018 ◽  
Vol 22 (4) ◽  
pp. 544-550 ◽  
Author(s):  
Yukihiro Hamasuna ◽  
Daiki Kobayashi ◽  
Ryo Ozaki ◽  
Yasunori Endo ◽  
◽  
...  
Keyword(s):  
Numerical Experiments ◽  
Optimal Number ◽  
Network Data ◽  
Clustering Methods ◽  
Cluster Validity ◽  
Evaluation Measures ◽  
Network Cluster ◽  
Validity Measures ◽  
Network Partitions ◽  
Optimal Number Of Clusters

Modularity is one of the evaluation measures for network partitions and is used as the merging criterion in the Louvain method. To construct useful cluster validity measures and clustering methods for network data, network cluster validity measures are proposed based on the traditional indices. The effectiveness of the proposed measures are compared and applied to determine the optimal number of clusters. The network cluster partitions of various network data which are generated from the Polaris dataset are obtained byk-medoids with Dijkstra’s algorithm and evaluated by the proposed measures as well as the modularity. Our numerical experiments show that the Dunn’s index and the Xie-Beni’s index-based measures are effective for network partitions compared to other indices.

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