A distributed and incremental algorithm for large-scale graph clustering

Graph clustering is one of the key techniques to understand structures that are presented in networks. In addition to clusters, bridges and outliers detection is also a critical task as it plays an important role in the analysis of networks. Recently, several graph clustering methods are developed a...

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Veröffentlicht in:Future generation computer systems Jg. 134; S. 334 - 347
Hauptverfasser: Inoubli, Wissem, Aridhi, Sabeur, Mezni, Haithem, Maddouri, Mondher, Mephu Nguifo, Engelbert
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2022
Elsevier
Schlagworte:
Artificial Intelligence
Big graph analysis
Bioinformatics
Community detection
Computer Science
Distributed, Parallel, and Cluster Computing
Graph processing
Hubs detection
Machine Learning
Outliers detection
Structural graph clustering
Community detection
Big graph analysis
Structural graph clustering
Graph processing
Hubs detection
Outliers detection
ISSN:0167-739X, 1872-7115
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Zusammenfassung:Graph clustering is one of the key techniques to understand structures that are presented in networks. In addition to clusters, bridges and outliers detection is also a critical task as it plays an important role in the analysis of networks. Recently, several graph clustering methods are developed and used in multiple application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on the structural clustering algorithm. Yet, this kind of algorithm is based on the structural similarity. This latter requires to parse all graph’ edges in order to compute the structural similarity. However, the height needs of similarity computing make this algorithm more adequate for small graphs, without significant support to deal with large-scale networks. In this paper, we propose a novel distributed graph clustering algorithm based on structural graph clustering. The experimental results show the efficiency in terms of running time of the proposed algorithm in large networks compared to existing structural graph clustering methods. •An adaptation of the edge partitioning method in a distributed setting.•A novel scalable clustering method for distributed networks.•An incremental graph clustering algorithm for both large and dynamic graphs.•An experimental study to evaluate the novel scalable clustering method for distributed networks.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2022.04.013