DENGRAPH: A Density-based Community Detection Algorithm

Spiliopoulos, Vassilis; Vouros, George A.; Karkaletsis, Vangelis

doi:10.1109/wi.2007.74

Cited by 68 publications

(68 citation statements)

References 2 publications

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“…Among them, DENGRAPH [10] and SCAN [11] algorithms are inspired by the density-based data clustering algorithm DBSCAN. They introduce two different distance functions in the clustering process.…”

Section: Related Workmentioning

confidence: 99%

A density-based clustering model for community detection in complex networks

Zhao¹,

Li²,

Qu³

2018

AIP Conference Proceedings

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Abstract. Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

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Section: Related Workmentioning

confidence: 99%

A density-based clustering model for community detection in complex networks

Zhao¹,

Li²,

Qu³

2018

AIP Conference Proceedings

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“…Given a graph G = (V,E) consisting of a set of nodes V and a set of weighted undirected edges E, DenGraph [5] algorithm produces clusters {C 1 ,..,C k } and noise nodes, that are not part of any cluster. Other non-noise nodes are either core nodes or border nodes.…”

Section: A Dengraphmentioning

confidence: 99%

“…DenGraph [5] is a density-based clustering algorithm for community detection in social networks, inspired by the wellknown clustering algorithm for spatial data, DBSCAN [6]. The main idea of DenGraph is to find clusters and outliers of weighted social networks, based on the interaction.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Community Detection Algorithm for Big Social Networks

AlQahtani¹,

Ykhlef²

2018

ijacsa

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Abstract-Mining social networks has become an important task in data mining field, which describes users and their roles and relationships in social networks. Processing social networks with graph algorithms is the source for discovering many features. The most important algorithms applied to social networks are community detection algorithms. Communities of social networks are groups of people sharing common interests or activities. DenGraph is one of the density-based algorithms that used to find clusters of arbitrary shapes based on users' interactions in social networks. However, because of the rapidly growing size of social networks, it is impossible to process a huge graph on a single machine in an acceptable level of execution. In this article, DenGraph algorithm has been redesigned to work in distributed computing environment. We proposed ParaDengraph Algorithm based on Pregel parallel model for large graph processing.

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“…Most of the optimization algorithms have used a modularity measure because it is a costly measure to process [12], [13], [14]. Other algorithms use a clustering principle [15] [16] and also find the overlapping communities, i.e. one node may be a part of several communities at once [17].…”

Section: Community Detectionmentioning

confidence: 99%

Anonymized Social Networks Community Preservation

Vadisala¹,

Kumari²

2017

ijacsa

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Abstract-Social Networks have been widely used in the society. Most of the people are connected to one another, communicated with each other and share the information in different forms. The information gathered from different social networking sites is growing tremendously in large volumes of various research, marketing and other purposes which is creating security and privacy concerns. The gathered information contains some sensitive and private information about an individual, such as the relationship of an individual or group information. So, to protect the data from unauthorized users the data should be anonymized before publishing. In this paper, we study how the k-degree and k-NMF anonymized methods preserve the existing communities of the original social networks. We use an existing heuristic algorithm called Louvian method to identify the communities in social networks. We conduct the experiments on real data sets and compare the performances of the two anonymized social networks for preservation of communities of the original social networks.

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DENGRAPH: A Density-based Community Detection Algorithm

Cited by 68 publications

References 2 publications

A density-based clustering model for community detection in complex networks

A density-based clustering model for community detection in complex networks

A Parallel Community Detection Algorithm for Big Social Networks

Anonymized Social Networks Community Preservation

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