A fast and efficient algorithm to identify clusters in networks

Comellas, Francesc; Miralles, Alícia

doi:10.1016/j.amc.2010.06.060

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…An edge in the graph represents the simultaneous appearance of the corresponding characters in one or more chapters of the novel. The communities computed by our model agree with those cited in the literature [25,26] (see Fig. 18).…”

Section: ''Les Misérables''supporting

confidence: 80%

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An O(n2) algorithm for detecting communities of unbalanced sizes in large scale social networks

Zardi

Romdhane

2013

Knowledge-Based Systems

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Section: ''Les Misérables''supporting

confidence: 80%

“…10). The computed [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] partitioning of the same graph by our algorithm MEP is depicted in Fig. 11 where we can easily see that the real structure composed of fifty cliques of the network was detected.…”

Section: Contributions Of Our Objective Function Compared To Modularitymentioning

confidence: 99%

An O(n2) algorithm for detecting communities of unbalanced sizes in large scale social networks

Zardi

Romdhane

2013

Knowledge-Based Systems

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“…Several community detection algorithms have been proposed in the literature; they are typically classified in: divisive algorithms (Girvan and Newman, 2002), agglomerative algorithms (Newman and Girvan, 2004;Comellas and Miralles, 2010) (depending on whether they focus on the addition or removal of edges to or from the network), and optimisation algorithms (Brandes et al, 2007) which continuously update the network partition in order to maximise the quality of the partition according to a given metric. Optimisation algorithms include several approaches: greedy routing (Newman, 2004), simulated annealing (Guimera et al, 2004), spectral optimisation (Newman, 2006a), game-theoretic (Narayanam and Narahari, 2010;Chen et al, 2010), compression-based (Rosvall and Bergstrom, 2007) and flow-based (Wu and Huberman, 2004).…”

Section: Introductionmentioning

confidence: 99%

Label propagation algorithm: a semi-synchronous approach

Cordasco

Gargano

2012

IJSNM

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Abstract:A recently introduced novel community detection strategy is based on a label propagation (LP) algorithm which uses the diffusion of information in the network to identify communities. Studies of LP algorithms showed that the strategy is effective in finding a good community structure. Label propagation step can be performed in parallel on all nodes (synchronous model) or sequentially (asynchronous model); both models present some drawback, e.g., algorithm termination is not granted in the first case, performances can be worst in the second case. In this paper, we present a semi-synchronous version of LP algorithms which aims to combine the advantages of both synchronous and asynchronous models. We prove that our models always converge to a stable labelling. Moreover, we experimentally investigate the effectiveness of the proposed strategy comparing its performance with the asynchronous model both in terms of quality, efficiency and stability. Tests show that the proposed protocol does not harm the quality of the partitioning. Moreover, it is quite efficient; each propagation step is extremely parallelisable and it is more stable than the asynchronous model, thanks to the fact that only a small amount of randomisation is used by our proposal.

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“…Thus clustering category [8][9][10][11][12] is shown in Table 1. Imputation is a process which imputes known or empirical value in missing data through some particular algorithms, and it has become a relatively popular approach to get complete data.…”

Section: Introductionmentioning

confidence: 99%

Clustering Algorithm for Incomplete Data Sets with Mixed Numeric and Categorical Attributes

Wu¹,

Chen²,

Xing³

2013

IJDTA

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The traditional k-prototypes algorithm is well versed in clustering data with mixed numeric and categorical attributes, while it is limited to complete data. In order to handle incomplete data set with missing values, an improved k-prototypes algorithm is proposed in this paper, which employs a new dissimilarity measure for incomplete data set with mixed numeric and categorical attributes and a new approach to select k objects as the initial prototypes based on the nearest neighbors. The improved k-prototypes algorithm can not only cluster incomplete data with no need to impute the missing values, but also avoid randomness in choosing initial prototypes. To illustrate the accuracy of the established algorithm, traditional k-prototypes algorithm and k-prototypes employing the new dissimilarity measure are compared to the improved k-prototypes algorithm by using data from UCI machine learning repository. The experimental results show that the improved k-prototypes algorithm is superior to the other two algorithms with higher clustering accuracy.

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A fast and efficient algorithm to identify clusters in networks

Cited by 7 publications

References 13 publications

An O(n2) algorithm for detecting communities of unbalanced sizes in large scale social networks

An O(n2) algorithm for detecting communities of unbalanced sizes in large scale social networks

Label propagation algorithm: a semi-synchronous approach

Clustering Algorithm for Incomplete Data Sets with Mixed Numeric and Categorical Attributes

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