Dynamic Boolean Matrix Factorizations

Miettinen, Pauli

doi:10.1109/icdm.2012.118

Cited by 12 publications

(17 citation statements)

References 17 publications

(25 reference statements)

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“…This section will present the extensions to allow the earlier algorithm of [10] to handle the removal of 1s and dynamic adjustments of the rank. We will start by presenting the algorithm of [10] before explaining how to extend it.…”

Section: Algorithmsmentioning

confidence: 99%

“…when it is only possible to add 1s, rows and columns. Here we will give a brief outline of the algorithm; for more information, see [10].…”

Section: Algorithm For Additive Dbmfmentioning

confidence: 99%

“…The algorithm of [10] works only in the additive dynamic BMF setting, i.e. when it is only possible to add 1s, rows and columns.…”

Section: Algorithm For Additive Dbmfmentioning

confidence: 99%

“…The basic algorithm in [10] is online, that is, it can only add 1s in the factor matrices, and can never remove them. When a 1 is added that is not yet covered by the factorization, the algorithm tries to find a factor to extend to cover the added 1.…”

Section: Algorithm For Additive Dbmfmentioning

confidence: 99%

“…The algorithm presented in this paper is an extension of a recent algorithm for dynamic Boolean matrix factorizations [10]. The algorithm of [10] has two shortcomings: 1) edges and nodes can only be added, never removed and 2) it cannot dynamically adjust the size of the covering (rank of the factorization). This paper will address both of these shortcomings.…”

Section: Introductionmentioning

confidence: 99%

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Fully dynamic quasi-biclique edge covers via Boolean matrix factorizations

Miettinen

2013

Proceedings of the Workshop on Dynamic Networks Management and Mining

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An important way of summarizing a bipartite graph is to give a set of (quasi-) bicliques that contain (almost) all of its edges. These quasi-bicliques are somewhat similar to clustering of the nodes, giving sets of similar nodes. Unlike clustering, however, the quasi-bicliques are not required to partition the nodes, allowing greater flexibility when creating them. When we identify the bipartite graph with its bi-adjacency matrix, the problem of finding these quasi-bicliques turns into the problem of finding the Boolean matrix factorization of the bi-adjacency matrix -a problem that has received increasing research interest in data mining in recent years. But many real-world graphs are dynamic and evolve over time. How can we update our bicliques without having to re-compute them from the scratch?An algorithm was recently proposed for this task (Miettinen, ICMD 2012). The algorithm, however, is only able to handle the case where the new 1s are added to the matrix -it cannot handle the removal of existing 1s. Furthermore, the algorithm cannot adjust the rank of the factorization. This paper extends said algorithm with the capability of working in fully dynamic setting (with both additions and deletions) and with capability of adjusting its rank dynamically, as well. The behaviour and performance of the algorithm is studied in experiments conducted with both real-world and synthetic data.

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Section: Algorithmsmentioning

confidence: 99%

“…when it is only possible to add 1s, rows and columns. Here we will give a brief outline of the algorithm; for more information, see [10].…”

Section: Algorithm For Additive Dbmfmentioning

confidence: 99%

“…The algorithm of [10] works only in the additive dynamic BMF setting, i.e. when it is only possible to add 1s, rows and columns.…”

Section: Algorithm For Additive Dbmfmentioning

confidence: 99%

Section: Algorithm For Additive Dbmfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fully dynamic quasi-biclique edge covers via Boolean matrix factorizations

Miettinen

2013

Proceedings of the Workshop on Dynamic Networks Management and Mining

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A Genetic Approach for Virtual Computer Network Design

Saenko

Kotenko

2015

Intelligent Distributed Computing VIII

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Abstract. One of possible levels of computer protection may consist in splitting computer networks into logical chunks that are known as virtual computer networks or virtual subnets. The paper considers a novel approach to determine virtual subnets that is based on the given matrix of logic connectivity of computers. The paper shows that the problem considered is related to one of the forms of Boolean Matrix Factorization. It formulates the virtual subnet design task and proposes genetic algorithms as a means to solve it. Basic improvements proposed in the paper are using trivial solutions to generate an initial population, taking into account in the fitness function the criterion of minimum number of virtual subnets, and using columns of the connectivity matrix as genes of chromosomes. Experimental results show the proposed genetic algorithm has high effectiveness.

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Reconfiguration of Access Schemes in Virtual Networks of the Internet of Things by Genetic Algorithms

Saenko

Kotenko

2015

Intelligent Distributed Computing IX

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Dynamic Boolean Matrix Factorizations

Cited by 12 publications

References 17 publications

Fully dynamic quasi-biclique edge covers via Boolean matrix factorizations

Fully dynamic quasi-biclique edge covers via Boolean matrix factorizations

A Genetic Approach for Virtual Computer Network Design

Reconfiguration of Access Schemes in Virtual Networks of the Internet of Things by Genetic Algorithms

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