Scalable and Interactive Graph Clustering Algorithm on Multicore CPUs

2017 ◽  
Author(s):  
Son T. Mai ◽  
Martin Storgaard Dieu ◽  
Ira Assent ◽  
Jon Jacobsen ◽  
Jesper Kristensen ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Graph Clustering ◽  
Multicore Cpus

Efficient Vector Partitioning Algorithms for Graph Clustering

2020 ◽  
Vol 1 (2) ◽  
pp. 101-123
Author(s):  
Hiroaki Shiokawa ◽  
Yasunori Futamura
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Ground Truth ◽  
Graph Clustering ◽  
Mining Communities ◽  
Fine Grained ◽  
Efficient Vector ◽  
Public Datasets ◽  
Many Core

This paper addressed the problem of finding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, \textit{CAV}, that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel efficient algorithm \textit{P-CAV} for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.

scholarly journals Implementasi Graph Clustering Algorithm Modification Maximum Standard Deviation Reduction (MMSDR) dalam Clustering Provinsi di Indonesia Menurut Indikator Kesejahteraan Rakyat

Faktor Exacta ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 73
Author(s):  
Nurfidah Dwitiyanti ◽  
Septian Wulandari ◽  
Noni Selvia
Keyword(s):  
Economic Growth ◽  
Standard Deviation ◽  
Clustering Algorithm ◽  
Graph Clustering ◽  
Public Welfare ◽  
Poor People ◽  
Equitable Distribution ◽  
Maximum Standard Deviation ◽  
Central Statistics

<p>The population of Indonesia from year to year has increased. The increase in population must also be accompanied by increased economic growth in Indonesia. The increase in economic growth in Indonesia is marked by the reduction in the number of poor people in Indonesia. In addition, the increase in economic growth is reflected in the equitable distribution of public income in the country. Even though there are still many Indonesian people who are not yet prosperous in economic terms. To overcome, it is necessary to have clustering and characteristics of 34 provinces in Indonesia by implementing the Modification Maximum Standard Deviation Reduction (MMSDR) graph clustering algorithm. The data used are indicators of public welfare in 2017 obtained from the Central Statistics Agency. There are 9 indicators of community welfare used in this research. There are four stages in the MMSDR algorithm namely the "MST", "Subdivide", "Biggest Stepping" and "Create Clusters" processes. The results of this study can be seen from the distance between the nodes or between one province and another province produced 22 clusters. From the cluster results obtained using the MMSDR algorithm on welfare data, there are many clusters formed with cluster members formed at most two nodes (province).</p><p> Keywords: MMSDR, Clustering, Welfare of People</p>

A consensus graph clustering algorithm for directed networks

2016 ◽  
Vol 54 ◽  
pp. 121-135 ◽  
Author(s):  
Camila Pereira Santos ◽  
Desiree Maldonado Carvalho ◽  
Mariá C.V. Nascimento
Keyword(s):  
Clustering Algorithm ◽  
Graph Clustering ◽  
Directed Networks
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