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Medková J, Hynek J. HAkAu: hybrid algorithm for effective k-automorphism anonymization of social networks. SOCIAL NETWORK ANALYSIS AND MINING 2023. [DOI: 10.1007/s13278-023-01064-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
AbstractOnline social network datasets contain a large amount of various information about their users. Preserving users’ privacy while publishing or sharing datasets with third parties has become a challenging problem. The k-automorphism is the anonymization method that protects the social network dataset against any passive structural attack. It provides a higher level of protection than other k-anonymity methods, including k-degree or k-neighborhood techniques. In this paper, we propose a hybrid algorithm that effectively modifies the social network to the k-automorphism one. The proposed algorithm is based on the structure of the previously published k-automorphism KM algorithm. However, it solves the NP-hard subtask of finding isomorphic graph extensions with a genetic algorithm and employs the GraMi algorithm for finding frequent subgraphs. In the design of the genetic algorithm, we introduce the novel chromosome representation in which the length of the chromosome is independent of the size of the input network, and each individual in each generation leads to the k-automorphism solution. Moreover, we present a heuristic method for selecting the set of vertex disjoint subgraphs. To test the algorithm, we run experiments on a set of real social networks and use the SecGraph tool to evaluate our results in terms of protection against deanonymization attacks and preserving data utility. It makes our experimental results comparable with any future research.
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Arnaud Droit,
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A Multi-Objective Genetic Algorithm for overlapping community detection based on edge encoding. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.06.015] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Liu C, Abu-Jamous B, Brattico E, Nandi AK. Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing. Int J Neural Syst 2016; 27:1650042. [DOI: 10.1142/s0129065716500428] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package “UNCLES” available on http://cran.r-project.org/web/packages/UNCLES/index.html .
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Affiliation(s)
- Chao Liu
- * Department of Electronic and Computer Engineering, Brunel University London, London, UK
| | - Basel Abu-Jamous
- * Department of Electronic and Computer Engineering, Brunel University London, London, UK
| | - Elvira Brattico
- † Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,‡ The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - Asoke K Nandi
- * Department of Electronic and Computer Engineering, Brunel University London, London, UK.,§ The Key Laboratory of Embedded Systems and Service Computing, College of Electronic and Information Engineering, Tongji University, Shanghai, P. R. China
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Martín A, Menéndez HD, Camacho D. MOCDroid: multi-objective evolutionary classifier for Android malware detection. Soft comput 2016. [DOI: 10.1007/s00500-016-2283-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Bello-Orgaz G, Jung JJ, Camacho D. Social big data: Recent achievements and new challenges. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2016; 28:45-59. [PMID: 32288689 PMCID: PMC7106299 DOI: 10.1016/j.inffus.2015.08.005] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 08/07/2015] [Accepted: 08/17/2015] [Indexed: 05/04/2023]
Abstract
Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms.
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Affiliation(s)
- Gema Bello-Orgaz
- Computer Science Department, Universidad Autónoma de Madrid, Spain
| | - Jason J. Jung
- Department of Computer Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - David Camacho
- Computer Science Department, Universidad Autónoma de Madrid, Spain
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Suberbiola A, Zulueta E, Lopez-Guede JM, Etxeberria-Agiriano I, Graña M. Arm Orthosis/Prosthesis Movement Control Based on Surface EMG Signal Extraction. Int J Neural Syst 2015; 25:1550009. [DOI: 10.1142/s0129065715500094] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper shows experimental results on electromyography (EMG)-based system control applied to motorized orthoses. Biceps and triceps EMG signals are captured through two biometrical sensors, which are then filtered and processed by an acquisition system. Finally an output/control signal is produced and sent to the actuators, which will then perform the actual movement, using algorithms based on autoregressive (AR) models and neural networks, among others. The research goal is to predict the desired movement of the lower arm through the analysis of EMG signals, so that the movement can be reproduced by an arm orthosis, powered by two linear actuators. In this experiment, best accuracy has achieved values up to 91%, using a fourth-order AR-model and 100ms block length.
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Affiliation(s)
- Aaron Suberbiola
- Department of Systems Engineering and Automatic Control, University College of Engineering of Vitoria, University of the Basque Country (UPV/EHU), Nieves Cano 12, Vitoria-Gasteiz, Spain
| | - Ekaitz Zulueta
- Department of Systems Engineering and Automatic Control, University College of Engineering of Vitoria, University of the Basque Country (UPV/EHU), Nieves Cano 12, Vitoria-Gasteiz, Spain
| | - Jose Manuel Lopez-Guede
- Department of Systems Engineering and Automatic Control, University College of Engineering of Vitoria, University of the Basque Country (UPV/EHU), Nieves Cano 12, Vitoria-Gasteiz, Spain
| | - Ismael Etxeberria-Agiriano
- Department of Computer Languages and Systems, University College of Engineering of Vitoria, University of the Basque Country (UPV/EHU), Nieves Cano 12, Vitoria-Gasteiz, Spain
| | - Manuel Graña
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), Paseo Manuel Lardizabal 1, Donostia-San Sebastian, Spain
- ENGINE Centre, Wroclay University of Technology (WTU), Poland
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Abstract
The spiral has universal appeal and a mysterious resonance with the human spirit. It is intriguing and beautiful. The spiral pattern is found extensively in nature, encoded into plants, animals, weather phenomena, the earth, and galaxies around us. The paper starts with a mathematical representation of the spiral phenomena. Next, 2-dimensional and n-dimensional Spiral Dynamics Optimization (SpDO) algorithms are presented followed by their variants such as adaptive SpDO and hybrid SpDO. Finally, applications to engineering problems are reviewed including a flexible robot manipulator system, training of artificial neural networks, the Combined Economic and Emission Dispatch (CEED) optimization problem in power system operations, and clustering in data mining.
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Affiliation(s)
- Nazmul Siddique
- School of Computing and Intelligent Systems, University of Ulster, Northland Road, Londonderry, BT48 7JL, UK
| | - Hojjat Adeli
- Department of Civil, Environmental, and Geodetic Engineering, Electrical and Computer Engineering, Biomedical Engineering, Biomedical Informatics, and Neuroscience, Columbus OH 43065, USA
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Abstract
This article presents a review of a novel nature-inspired adaptive optimization algorithm known as Water Drop Algorithm (WDA) which imitates the dynamics of river systems and the behavior of water drops when they are moving in a river system such as the variation of velocity, the change of sediment/soil in the river bed, the change of direction of the flow, and so on. Recent improved, modified, and adaptive WDAs are examined in this paper. Various applications of WDA are described briefly including the vehicle routing problem, economic load dispatch problem, economic and emission dispatch problem, optimal data aggregation tree in wireless networks, reactive power dispatch problem service selection, and reservoir operation.
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Affiliation(s)
- Nazmul Siddique
- School of Computing and Intelligent Systems, University of Ulster Northland Road Londonderry, BT48 7JL, UK
| | - Hojjat Adeli
- College of Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210, USA
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Ahmadlou M, Adeli A, Bajo R, Adeli H. Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task. Clin Neurophysiol 2014; 125:694-702. [DOI: 10.1016/j.clinph.2013.08.033] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 07/30/2013] [Accepted: 08/06/2013] [Indexed: 01/25/2023]
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MENÉNDEZ HÉCTORD, BARRERO DAVIDF, CAMACHO DAVID. A GENETIC GRAPH-BASED APPROACH FOR PARTITIONAL CLUSTERING. Int J Neural Syst 2014; 24:1430008. [DOI: 10.1142/s0129065714300083] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.
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Affiliation(s)
- HÉCTOR D. MENÉNDEZ
- Computer Science Department, Universidad Autónoma de Madrid, 28049, Madrid, Spain
| | - DAVID F. BARRERO
- Departamento de Automática, Universidad de Alcalá, 28801, Alcalá de Henares, Madrid, Spain
| | - DAVID CAMACHO
- Computer Science Department, Universidad Autónoma de Madrid, 28049, Madrid, Spain
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GONÇALVES NICOLAU, NIKKILÄ JANNE, VIGÁRIO RICARDO. SELF-SUPERVISED MRI TISSUE SEGMENTATION BY DISCRIMINATIVE CLUSTERING. Int J Neural Syst 2013; 24:1450004. [DOI: 10.1142/s012906571450004x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The study of brain lesions can benefit from a clear identification of transitions between healthy and pathological tissues, through the analysis of brain imaging data. Current signal processing methods, able to address these issues, often rely on strong prior information. In this article, a new method for tissue segmentation is proposed. It is based on a discriminative strategy, in a self-supervised machine learning approach. This method avoids the use of prior information, which makes it very versatile, and able to cope with different tissue types. It also returns tissue probabilities for each voxel, crucial for a good characterization of the evolution of brain lesions. Simulated as well as real benchmark data were used to validate the accuracy of the method and compare it against other segmentation algorithms.
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Affiliation(s)
- NICOLAU GONÇALVES
- Department of Information and Computer Science, Aalto University School of Science, P. O. Box 15400, FI-00076 Aalto, Espoo, Finland
| | | | - RICARDO VIGÁRIO
- Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, FI-00076 Aalto, Espoo, Finland
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Abstract
This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.
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Affiliation(s)
- Alex Alexandridis
- Department of Electronics, Technological Educational Institute of Athens, Agiou Spiridonos Aigaleo 12210, Greece
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Li G, Liu F. Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions. APPL INTELL 2013. [DOI: 10.1007/s10489-013-0463-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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