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  1. Artikel ; Online: Random Hyperboxes.

    Khuat, Thanh Tung / Gabrys, Bogdan

    IEEE transactions on neural networks and learning systems

    2023  Band 34, Heft 2, Seite(n) 1008–1022

    Abstract: This article proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set. We also show a ... ...

    Abstract This article proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set. We also show a generalization error bound of the proposed classifier based on the strength of the individual hyperbox-based classifiers as well as the correlation among them. The effectiveness of the proposed classifier is analyzed using a carefully selected illustrative example and compared empirically with other popular single and ensemble classifiers via 20 datasets using statistical testing methods. The experimental results confirmed that our proposed method outperformed other fuzzy min-max neural networks (FMNNs), popular learning algorithms, and is competitive with other ensemble methods. Finally, we identify the existing issues related to the generalization error bounds of the real datasets and inform the potential research directions.
    Sprache Englisch
    Erscheinungsdatum 2023-02-03
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2021.3104896
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Towards Digital Twin-Oriented Complex Networked Systems: Introducing heterogeneous node features and interaction rules.

    Wen, Jiaqi / Gabrys, Bogdan / Musial, Katarzyna

    PloS one

    2024  Band 19, Heft 1, Seite(n) e0296426

    Abstract: This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real-world social networked systems. Modelling process focuses on (i) ... ...

    Abstract This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real-world social networked systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.
    Mesh-Begriff(e) Humans ; Computer Simulation ; Disasters ; Epidemics ; Disease Susceptibility
    Sprache Englisch
    Erscheinungsdatum 2024-01-02
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0296426
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Evolutionary Digital Twin-Oriented Complex Networked Systems driven by node features and the mutation of feature preferences.

    Wen, Jiaqi / Gabrys, Bogdan / Musial, Katarzyna

    PloS one

    2024  Band 19, Heft 5, Seite(n) e0303571

    Abstract: Accurate modelling of complex social systems, where people interact with each other and those interactions change over time, has been a research challenge for many years. This study proposes an evolutionary Digital Twin-Oriented Complex Networked System ( ...

    Abstract Accurate modelling of complex social systems, where people interact with each other and those interactions change over time, has been a research challenge for many years. This study proposes an evolutionary Digital Twin-Oriented Complex Networked System (DT-CNS) framework that considers heterogeneous node features and changeable connection preferences. We create heterogeneous preference mutation mechanisms to characterise nodes' adaptive decisions on preference mutation in response to interaction patterns and epidemic risks. In this space, we use nodes' interaction utilities to characterise the positive feedback from interactions and negative impact of epidemic risks. We also introduce social capital constraint to harness the density of social connections better. The nodes' heterogeneous preference mutation styles include the (i)inactive style that keeps initial social preferences, (ii) ignorant style that randomly mutates preferences, (iii) egocentric style that optimises individual interaction utility, (iv) cooperative style that optimises the total interaction utilities by group decisions and (v) collaborative style that further allows the cooperative nodes to transfer social capital. Our simulation experiments on evolutionary DT-CNSs reveal that heterogeneous preference mutation styles lead to various interaction and infection patterns. The results also show that (i) increasing social capital enables higher interactions but higher infection risks and uncertainty in decision-making; (ii) group decisions outperform individual decisions by eliminating the unawareness of the decisions of other nodes; (iii) the collaborative nodes under a strict social capital limit can promote interactions, reduce infection risks and achieve higher overall interaction utilities.
    Mesh-Begriff(e) Humans ; Mutation ; Computer Simulation ; Models, Theoretical ; Biological Evolution ; Social Networking
    Sprache Englisch
    Erscheinungsdatum 2024-05-16
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0303571
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  4. Artikel ; Online: NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size.

    Dong, Xuanyi / Liu, Lu / Musial, Katarzyna / Gabrys, Bogdan

    IEEE transactions on pattern analysis and machine intelligence

    2022  Band 44, Heft 7, Seite(n) 3634–3646

    Abstract: Neural architecture search (NAS) has attracted a lot of attention and has been illustrated to bring tangible benefits in a large number of applications in the past few years. Architecture topology and architecture size have been regarded as two of the ... ...

    Abstract Neural architecture search (NAS) has attracted a lot of attention and has been illustrated to bring tangible benefits in a large number of applications in the past few years. Architecture topology and architecture size have been regarded as two of the most important aspects for the performance of deep learning models and the community has spawned lots of searching algorithms for both of those aspects of the neural architectures. However, the performance gain from these searching algorithms is achieved under different search spaces and training setups. This makes the overall performance of the algorithms incomparable and the improvement from a sub-module of the searching model unclear. In this paper, we propose NATS-Bench, a unified benchmark on searching for both topology and size, for (almost) any up-to-date NAS algorithm. NATS-Bench includes the search space of 15,625 neural cell candidates for architecture topology and 32,768 for architecture size on three datasets. We analyze the validity of our benchmark in terms of various criteria and performance comparison of all candidates in the search space. We also show the versatility of NATS-Bench by benchmarking 13 recent state-of-the-art NAS algorithms on it. All logs and diagnostic information trained using the same setup for each candidate are provided. This facilitates a much larger community of researchers to focus on developing better NAS algorithms in a more comparable and computationally effective environment. All codes are publicly available at: https://xuanyidong.com/assets/projects/NATS-Bench.
    Mesh-Begriff(e) Algorithms ; Benchmarking ; Neural Networks, Computer ; Neurons ; Plant Extracts
    Chemische Substanzen Plant Extracts ; nas (64706-31-6)
    Sprache Englisch
    Erscheinungsdatum 2022-06-03
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2021.3054824
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Buch ; Online: Review and Assessment of Digital Twin--Oriented Social Network Simulators

    Wen, Jiaqi / Gabrys, Bogdan / Musial, Katarzyna

    2023  

    Abstract: The ability to faithfully represent real social networks is critical from the perspective of testing various what-if scenarios which are not feasible to be implemented in a real system as the system's state would be irreversibly changed. High fidelity ... ...

    Abstract The ability to faithfully represent real social networks is critical from the perspective of testing various what-if scenarios which are not feasible to be implemented in a real system as the system's state would be irreversibly changed. High fidelity simulators allow one to investigate the consequences of different actions before introducing them to the real system. For example, in the context of social systems, an accurate social network simulator can be a powerful tool used to guide policy makers, help companies plan their advertising campaigns or authorities to analyse fake news spread. In this study we explore different Social Network Simulators (SNSs) and assess to what extent they are able to mimic the real social networks. We conduct a critical review and assessment of existing Social Network Simulators under the Digital Twin-Oriented Modelling framework proposed in our previous study. We subsequently extend one of the most promising simulators from the evaluated ones, to facilitate generation of social networks of varied structural complexity levels. This extension brings us one step closer to a Digital Twin Oriented SNS (DT Oriented SNS). We also propose an approach to assess the similarity between real and simulated networks with the composite performance indexes based on both global and local structural measures, while taking runtime of the simulator as an indicator of its efficiency. We illustrate various characteristics of the proposed DT Oriented SNS using a well known Karate Club network as an example. While not considered to be of sufficient complexity, the simulator is intended as one of the first steps on a journey towards building a Digital Twin of a social network that perfectly mimics the reality.
    Schlagwörter Computer Science - Social and Information Networks
    Erscheinungsdatum 2023-05-04
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Buch ; Online: Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules

    Wen, Jiaqi / Gabrys, Bogdan / Musial, Katarzyna

    2023  

    Abstract: This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real systems. Modelling process focuses on (i) features of nodes and (ii) ... ...

    Abstract This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.
    Schlagwörter Computer Science - Social and Information Networks ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 612 ; 006
    Erscheinungsdatum 2023-08-17
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Buch ; Online: Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems

    Wen, Jiaqi / Gabrys, Bogdan / Musial, Katarzyna

    2023  

    Abstract: Building models of Complex Networked Systems (CNS) that can accurately represent reality forms an important research area. To be able to reflect real world systems, the modelling needs to consider not only the intensity of interactions between the ... ...

    Abstract Building models of Complex Networked Systems (CNS) that can accurately represent reality forms an important research area. To be able to reflect real world systems, the modelling needs to consider not only the intensity of interactions between the entities but also features of all the elements of the system. This study aims to improve the expressive power of node features in Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with heterogeneous feature representation principles. This involves representing features with crisp feature values and fuzzy sets, each describing the objective and the subjective inductions of the nodes' features and feature differences. Our empirical analysis builds DT-CNSs to recreate realistic physical contact networks in different countries from real node feature distributions based on various representation principles and an optimised feature preference. We also investigate their respective disaster resilience to an epidemic outbreak starting from the most popular node. The results suggest that the increasing flexibility of feature representation with fuzzy sets improves the expressive power and enables more accurate modelling. In addition, the heterogeneous features influence the network structure and the speed of the epidemic outbreak, requiring various mitigation policies targeted at different people.
    Schlagwörter Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2023-09-22
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Buch ; Online: A Network Science perspective of Graph Convolutional Networks

    Jia, Mingshan / Gabrys, Bogdan / Musial, Katarzyna

    A survey

    2023  

    Abstract: The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of ... ...

    Abstract The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research.
    Schlagwörter Computer Science - Social and Information Networks ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-01-12
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  9. Artikel ; Online: Directed closure coefficient and its patterns.

    Jia, Mingshan / Gabrys, Bogdan / Musial, Katarzyna

    PloS one

    2021  Band 16, Heft 6, Seite(n) e0253822

    Abstract: The triangle structure, being a fundamental and significant element, underlies many theories and techniques in studying complex networks. The formation of triangles is typically measured by the clustering coefficient, in which the focal node is the ... ...

    Abstract The triangle structure, being a fundamental and significant element, underlies many theories and techniques in studying complex networks. The formation of triangles is typically measured by the clustering coefficient, in which the focal node is the centre-node in an open triad. In contrast, the recently proposed closure coefficient measures triangle formation from an end-node perspective and has been proven to be a useful feature in network analysis. Here, we extend it by proposing the directed closure coefficient that measures the formation of directed triangles. By distinguishing the direction of the closing edge in building triangles, we further introduce the source closure coefficient and the target closure coefficient. Then, by categorising particular types of directed triangles (e.g., head-of-path), we propose four closure patterns. Through multiple experiments on 24 directed networks from six domains, we demonstrate that at network-level, the four closure patterns are distinctive features in classifying network types, while at node-level, adding the source and target closure coefficients leads to significant improvement in link prediction task in most types of directed networks.
    Mesh-Begriff(e) Algorithms ; Models, Theoretical
    Sprache Englisch
    Erscheinungsdatum 2021-06-25
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0253822
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel ; Online: Examining the Role of Structural and Functional Social Network Characteristics in the Context of Chronic Pain: An Ego-centered Network Design.

    Van Alboom, Maité / Baert, Fleur / Bernardes, Sónia F / Verhofstadt, Lesley / Bracke, Piet / Jia, Mingshan / Musial, Katarzyna / Gabrys, Bogdan / Goubert, Liesbet

    The journal of pain

    2024  , Seite(n) 104525

    Abstract: The well-being and functioning of individuals with chronic pain (CP) vary significantly. Social factors, such as social integration, may help explain this differential impact. Specifically, structural (network size, density) as well as functional ( ... ...

    Abstract The well-being and functioning of individuals with chronic pain (CP) vary significantly. Social factors, such as social integration, may help explain this differential impact. Specifically, structural (network size, density) as well as functional (perceived social support, conflict) social network characteristics may play a role. However, it is not yet clear whether and how these variables are associated with each other. Objectives were to examine 1) both social network characteristics in individuals with primary and secondary CP, 2) the association between structural network characteristics and mental distress and functioning/participation in daily life, and 3) whether the network's functionality mediated the association between structural network characteristics and mental distress, respectively, functioning/participation in daily life. Using an online ego-centered social network tool, cross-sectional data were collected from 303 individuals with CP (81.85% women). No significant differences between individuals with fibromyalgia versus secondary CP were found regarding network size and density. In contrast, ANCOVA models showed lower levels of perceived social support and higher levels of conflict in primary (vs secondary) CP. Structural equation models showed that 1) larger network size indirectly predicted lower mental distress via lower levels of conflict; 2) higher network density increased mental distress via the increase of conflict levels. Network size or density did not (in)directly predict functioning/participation in daily life. The findings highlight that the role of conflict, in addition to support, should not be underestimated as a mediator for mental well-being. Research on explanatory mechanisms for associations between the network's structure, functionality, and well-being is warranted. PERSPECTIVE: This paper presents results on associations between structural (network size, density) and functional (social support, conflict) social network characteristics and well-being in the context of CP by making use of an ego-centered network design. Results suggest an indirect association between structural network characteristics and individuals with CP their mental well-being.
    Sprache Englisch
    Erscheinungsdatum 2024-04-10
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2018789-0
    ISSN 1528-8447 ; 1526-5900
    ISSN (online) 1528-8447
    ISSN 1526-5900
    DOI 10.1016/j.jpain.2024.104525
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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