AHSCAN: Agglomerative Hierarchical Structural Clustering Algorithm for Networks

Yuruk, Nurcan; Mete, Mutlu; Xu, Xiaowei; Schweiger, Thomas A. J.

doi:10.1109/asonam.2009.74

Cited by 26 publications

(13 citation statements)

References 7 publications

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“…SHRINK-G is faster but it sacrifices the capacity of finding hierarchical community structure. AHSCAN [11] performs agglomerative hierarchical clustering by iteratively merging pair of vertices in order of decreasing structural similarity of vertices, until a single cluster remains. AHSCAN selects the partition that maximizes the modularity, so its time complexity scales by O(|E||V|).…”

Section: Related Workmentioning

confidence: 99%

“…The community detection problem has been considered as the discovering of dense sub graphs defined by the structural similarity of vertices. This similarity has proved to be very effective in modeling such dense sub graphs [4,8,11,12,13]. We propose a novel fast heuristic algorithm inspired on an agglomerative hierarchical technique by using this structural similarity measure.…”

Section: Community Detection Algorithmmentioning

confidence: 99%

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Fast Heuristic Algorithm for Multi-scale Hierarchical Community Detection

Castrillo

León

Gómez

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Abstract-Complex networks constitute the backbones of many complex systems such as social networks. Detecting the community structure in a complex network is both a challenging and a computationally expensive task. In this paper, we present the HAMUHI-CODE, a novel fast heuristic algorithm for multiscale hierarchical community detection inspired on an agglomerative hierarchical clustering technique. We define a new structural similarity of vertices based on the classical cosine similarity by removing some vertices in order to increase the probability of identifying inter-cluster edges. Then we use the proposed structural similarity in a new agglomerative hierarchical algorithm that does not merge only clusters with maximal similarity as in the classical approach, but merges any cluster that does not meet a parameterized community definition with its most similar adjacent cluster. The algorithm computes all the similar clusters at the same time is checking if each cluster meets the parameterized community definition. It is done in linear time complexity in terms of the number of cluster in the iteration. Since a complex network is a sparse graph, our approach HAMUHI-CODE has a super-linear time complexity with respect to the size of the input in the worst-case scenario (if the clusters merge in pairs), making it suitable to be applied on large-scale complex networks. To test the properties and the efficiency of our algorithm we have conducted extensive experiments on real world and synthetic benchmark networks by comparing it to several baseline state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Community Detection Algorithmmentioning

confidence: 99%

Fast Heuristic Algorithm for Multi-scale Hierarchical Community Detection

Castrillo

León

Gómez

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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“…The similarity of two nodes correlates to the number of neighbours they share. Later, SCAN's similarity function was also used for the divisive hierarchical clustering (DHSCAN) and the agglomerative hierarchical clustering (AHSCAN) (Yuruk et al, 2009).…”

Section: Related and Previous Workmentioning

confidence: 99%

DenGraph‐HO: a density‐based hierarchical graph clustering algorithm

2013

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DenGraph‐HO is an extension of the density‐based graph clustering algorithm DenGraph. It is able to detect dense groups of nodes in a given graph and produces a hierarchy of clusters, which can be efficiently computed. The generated hierarchy can be used to investigate the structure and the characteristics of social networks. Each hierarchy level provides a different level of detail and can be used as the basis for interactive visual social network analysis. After a short introduction of the original DenGraph algorithm, we present DenGraph‐HO and its top‐down and bottom‐up approaches. We describe the data structures and memory requirements and analyse the run‐time complexity. Finally, we apply the DenGraph‐HO algorithm to the real‐world datasets obtained from the online music platform Last.fm and from the former US company Enron.

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“…Clustering is a useful technique for grouping schemas such that schemas within the same domain are similar while schemas in different domains are dissimilar. The clustering algorithm presented in this paper is an agglomerative hierarchical algorithm mainly extended from the SCAN approach [11]. The hierarchical clustering algorithm is appropriate for this clustering task since in general we do not know the number of clusters in advance.…”

Section: The Clustering Algorithmmentioning

confidence: 99%

Feature-based Clustering of Web Data Sources

Algergawy¹

2012

IJCA

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The proliferation of web data sources increasingly demands the integration of these sources. To facilitate the integration process, a pre-analysis step is required to classify and group data sources into their correct domains. In this paper, we propose a feature-based clustering approach for clustering web data sources without any human intervention and based only on features extracted from the source schemas. In particular, we make use of both linguistic and structural schema features. We experimentally demonstrate the effectiveness of the proposed approach in terms of both the clustering quality and runtime.

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AHSCAN: Agglomerative Hierarchical Structural Clustering Algorithm for Networks

Cited by 26 publications

References 7 publications

Fast Heuristic Algorithm for Multi-scale Hierarchical Community Detection

Fast Heuristic Algorithm for Multi-scale Hierarchical Community Detection

DenGraph‐HO: a density‐based hierarchical graph clustering algorithm

Feature-based Clustering of Web Data Sources

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