Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks

Matta, John; Obafemi-Ajayi, Tayo; Borwey, Jeffrey; Sinha, Koushik; Wunsch, Donald C.; Ercal, Gunes

doi:10.3390/app8081307

Cited by 12 publications

(24 citation statements)

References 55 publications

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“…This greedy heuristic can be used to optimize VAT, integrity and tenacity with acceptable accuracy 32,37 . Of the three resilience measures 30 , VAT tends to yield the smallest attack set while tenacity yields the largest. A consequence of using the Greedy-BC heuristic is that the three attack sets are related as follows:…”

Section: Critical Attack Set Scoringmentioning

confidence: 99%

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Biomarker discovery in inflammatory bowel diseases using network-based feature selection

Abbas

Matta

et al. 2019

Preprint

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Reliable identification of inflammatory biomarkers from metagenomics data is a promising direction for developing non-invasive, cost-effective, and rapid clinical tests for early diagnosis of IBD. We present an integrative approach to Network-Based Biomarker Discovery (NBBD) which integrates network analyses methods for prioritizing potential biomarkers and machine learning techniques for assessing the discriminative power of the prioritized biomarkers. Using a large dataset of new-onset pediatric IBD metagenomics biopsy samples, we compare the performance of Random Forest (RF) classifiers trained on features selected using a representative set of traditional feature selection methods against NBBD framework, configured using five different tools for inferring networks from metagenomics data, and nine different methods for prioritizing biomarkers as well as a hybrid approach combining best traditional and NBBD based feature selection. We also examine how the performance of the predictive models for IBD diagnosis varies as a function of the size of the data used for biomarker identification. Our results show that (i) NBBD is competitive with some of the state-of-the-art feature selection methods including Random Forest Feature Importance (RFFI) scores; and (ii) NBBD is especially effective in reliably identifying IBD biomarkers when the number of data samples available for biomarker discovery is small. 2/15 10/15 11/15 15/15

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Section: Critical Attack Set Scoringmentioning

confidence: 99%

“…Critical Attack Set Scoring (CASS) is based on a node resilience clustering algorithm, NBR-Clust 21,30 . We briefly describe below, the node resilience measures (specifically the three utilized in this work) before proceeding to describe how they are used to identify biomarkers.…”

Section: Critical Attack Set Scoringmentioning

confidence: 99%

Biomarker discovery in inflammatory bowel diseases using network-based feature selection

Abbas

Matta

et al. 2019

Preprint

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“…These, in turn, generate a bewildering profusion of data about users' activity in and use of digital devices [1,2] and it is now becoming ever clearer that data, particularly data sharing, is a dominant paradigm in an interconnected world [1]. This sharing is fostered by customer or client-centric platforms that support and encourage user generated content (UGC) [2], defined as content distributed via social networks by users who share publicly their opinions and comments on topics of interest [3].…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, the mass of UGC data overwhelms the means to extract timely, high-quality information-based insights that are meaningful, useful, efficient or applicable to managerial interventions [3][4][5][6]. Nevertheless, the desire to benefit from such data is driving researchers to seek new tools and analysis techniques that focus on identifying knowledge and insight generation [1,7].…”

Section: Introductionmentioning

confidence: 99%

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

2019

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New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization, and understanding. Education is the keystone area used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communication technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of user generated content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily available UGC, in this case the text published in tweets from the social network Twitter. The first stage employs topic-modeling using LDA (latent dirichlet allocation) to identify topics, which are then subjected to sentiment analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector in a short-term future.

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“…Matta et al [6] examined a scheme for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an object based on a critical set of nodes whose removal causes some severity of disconnection in the network.…”

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confidence: 99%

Complex Networks and Machine Learning: From Molecular to Social Sciences

et al. 2019

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Combining complex networks analysis methods with machine learning (ML) algorithms have become a very useful strategy for the study of complex systems in applied sciences. Noteworthy, the structure and function of such systems can be studied and represented through the above-mentioned approaches, which range from small chemical compounds, proteins, metabolic pathways, and other molecular systems, to neuronal synapsis in the brain's cortex, ecosystems, the internet, markets, social networks, program's development in education, social learning, etc. On the other hand, ML algorithms are useful to study large datasets with characteristic features of complex systems. In this context, we decided to launch one special issue focused on the benefits of using ML and complex network analysis (in combination or separately) to study complex systems in applied sciences. The topic of the issue is: Complex Networks and Machine Learning in Applied Sciences. Contributions to this special issue are highlighted below. The present issue is also linked to conference series, MOL2NET International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI AG, SciForum, Basel, Switzerland. At the same time, the special issue and the conference are hosts for the works published by students/tutors of the USEDAT: USA-Europe Data Analysis Training Worldwide Program.Combining complex networks analysis methods with machine learning (ML) algorithms have become a very useful strategy for the study of complex systems in applied sciences. Noteworthy, the structure and function of such systems can be studied and represented through the above-mentioned approaches, which range from small chemical compounds, proteins, metabolic pathways, and other molecular systems, to neuronal synapsis in the brain's cortex, ecosystems, the internet, markets, social

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Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks

Cited by 12 publications

References 55 publications

Biomarker discovery in inflammatory bowel diseases using network-based feature selection

Biomarker discovery in inflammatory bowel diseases using network-based feature selection

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

Complex Networks and Machine Learning: From Molecular to Social Sciences

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