scholarly journals Application of Multilayer Network Models in Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuanyuan Lv ◽  
Shan Huang ◽  
Tianjiao Zhang ◽  
Bo Gao
Keyword(s):  
Evaluation Method ◽  
Brain Research ◽  
Single Layer ◽  
Network Models ◽  
Assessment Method ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Biological Studies ◽  
Quality Assessment Method ◽  
Object Of Study

Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.

scholarly journals Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8305
Author(s):  
César Covantes-Osuna ◽  
Jhonatan B. López ◽  
Omar Paredes ◽  
Hugo Vélez-Pérez ◽  
Rebeca Romo-Vázquez
Keyword(s):  
Motor Imagery ◽  
Brain Connectivity ◽  
Single Layer ◽  
Network Models ◽  
Brain Regions ◽  
Adaptive Threshold ◽  
Brain Network ◽  
Multilayer Network ◽  
Brain Models ◽  
The Brain

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

Complex Systems as Multilayer Networks

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Complex Systems ◽  
Information Gain ◽  
Single Layer ◽  
Multilayer Networks ◽  
Research Activity ◽  
Multilayer Network ◽  
New Development ◽  
The Rich ◽  
And Function

Chapter 1 constitutes Part I of the book: ‘Single and Multilayer Networks’. This chapter introduces multilayer networks as an important new development of Network Science that allows a more comprehensive understanding of Complex Systems. It identifies the main motivations driving the research activity in this field of multilayer networks and emphasizes the benefits of taking a multilayer network perpective to characterize network data. The main advantages of a multilayer network approach with respect to the more traditional single layer characterization of complex networks are broadly discussed, focusing on the information gain resulting from the analysis of multilayer networks, the non-reducibility of a multilayer network to a large single network and the rich interplay between structure and function in multilayer networks.

Opinion Dynamics and Game Theory

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Game Theory ◽  
Social Systems ◽  
Single Layer ◽  
Opinion Dynamics ◽  
Structure And Function ◽  
Voter Model ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
The Rich ◽  
And Function

This chapter is devoted to opinion dynamics and game theory on multilayer networks. Since in social systems multilayer networks are the rule, it is particularly relevant to extend the modelling opinion dynamics to the multilayer network scenario. This chapter focuses in particular on the Voter Model, its variants, the Co-evolving Voter Model and models of competing networks, including election models showing that multiplexity has a major role in determining opinion dynamics. In particular, opinion dynamics on multilayer networks is not reducible to opinion dynamics on single layer networks. Finally, the rich interplay between structure and function in multilayer networks is discussed in the framework of game theory.

scholarly journals A survey of community detection methods in multilayer networks

2020 ◽  
Author(s):  
Xinyu Huang ◽  
Dongming Chen ◽  
Tao Ren ◽  
Dongqi Wang
Keyword(s):  
Community Detection ◽  
Rapid Development ◽  
Single Layer ◽  
Detection Methods ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Algorithm Efficiency ◽  
Comparison Results ◽  
Strongly Connected ◽  
Single Layer Network

Abstract Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there’s an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.

scholarly journals A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Hyobin Kim ◽  
Omar K. Pineda ◽  
Carlos Gershenson
Keyword(s):  
Complex Systems ◽  
Real World ◽  
Multilayer Structure ◽  
Boolean Network ◽  
Single Layer ◽  
Network Models ◽  
Boolean Networks ◽  
Multilayer Structures ◽  
Multilayer Networks ◽  
Engineered Systems

Antifragility is a property from which systems are able to resist stress and furthermore benefit from it. Even though antifragile dynamics is found in various real-world complex systems where multiple subsystems interact with each other, the attribute has not been quantitatively explored yet in those complex systems which can be regarded as multilayer networks. Here we study how the multilayer structure affects the antifragility of the whole system. By comparing single-layer and multilayer Boolean networks based on our recently proposed antifragility measure, we found that the multilayer structure facilitated the production of antifragile systems. Our measure and findings will be useful for various applications such as exploring properties of biological systems with multilayer structures and creating more antifragile engineered systems.

The Application of Fuzzy Comprehensive Evaluation Method in the Evaluation of Thesis Quality

2012 ◽  
Vol 263-266 ◽  
pp. 3501-3504
Author(s):  
Hua Wang ◽  
Ning Ning Chen ◽  
Jing Chen
Keyword(s):  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Method ◽  
Fuzzy Evaluation ◽  
Fuzzy Comprehensive Evaluation Method ◽  
Comprehensive Evaluation Method ◽  
Quality Assessment Method ◽  
Fuzzy Evaluation Model

For the drawbacks of the thesis quality assessment method which now currently used, Fuzzy evaluation model is constructed based on fuzzy comprehensive evaluation method, to evaluate the thesis. For the model we give a test experiment with the actual data. The results show that the evaluation results are more scientific and reasonable, for using the model of fuzzy evaluation.

scholarly journals Multilayer network analyses as a toolkit for measuring social structure

2021 ◽  
Vol 67 (1) ◽  
pp. 81-99
Author(s):  
Kelly R Finn
Keyword(s):  
Social Structure ◽  
Social Systems ◽  
Deep Understanding ◽  
Single Layer ◽  
Mathematical Representation ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Network Analyses ◽  
Different Types ◽  
Multilayer Social Networks

Abstract The formalization of multilayer networks allows for new ways to measure sociality in complex social systems, including groups of animals. The same mathematical representation and methods are widely applicable across fields and study systems, and a network can represent drastically different types of data. As such, in order to apply analyses and interpret the results in a meaningful way the researcher must have a deep understanding of what their network is representing and what parts of it are being measured by a given analysis. Multilayer social networks can represent social structure with more detail than is often present in single layer networks, including multiple “types” of individuals, interactions, or relationships, and the extent to which these types are interdependent. Multilayer networks can also encompass a wider range of social scales, which can help overcome complications that are inherent to measuring sociality. In this paper, I dissect multilayer networks into the parts that correspond to different components of social structures. I then discuss common pitfalls to avoid across different stages of multilayer network analyses—some novel and some that always exist in social network analysis but are magnified in multi-layer representations. This paper serves as a primer for building a customized toolkit of multilayer network analyses, to probe components of social structure in animal social systems.

Diffusion

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Random Walks ◽  
Spectral Properties ◽  
Diffusion Constant ◽  
Network Structures ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Lévy Walk ◽  
Number Of Layers ◽  
Levy Walk ◽  
Speed Up

This chapter addresses diffusion, random walks and congestion in multilayer networks. Here it is revealed that diffusion on a multilayer network can be significantly speed up with respect to diffusion taking place on its single layers taken in isolation, and that sometimes it is possible also to observe super-diffusion. Diffusion is here characterized on multilayer network structures by studying the spectral properties of the supra-Laplacian and the dependence on the diffusion constant among different layers. Random walks and its variations including the Lévy Walk are shown to reflect the improved navigability of multilayer networks with more layers. These results are here compared with the results of traffic on multilayer networks that, on the contrary, point out that increasing the number of layers could be detrimental and could lead to congestion.

Centrality Measures

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
State Of The Art ◽  
Centrality Measures ◽  
Eigenvector Centrality ◽  
Multilayer Networks ◽  
Comprehensive Description ◽  
Multilayer Network ◽  
Suitable Definition ◽  
Katz Centrality ◽  
Pagerank Centrality

Defining the centrality of nodes and layers in multilayer networks is of fundamental importance for a variety of applications from sociology to biology and finance. This chapter presents the state-of-the-art centrality measures able to characterize the centrality of nodes, the influences of layers or the centrality of replica nodes in multilayer and multiplex networks. These centrality measures include modifications of the eigenvector centrality, Katz centrality, PageRank centrality and Communicability to the multilayer network scenario. The chapter provides a comprehensive description of the research of the field and discusses the main advantages and limitations of the different definitions, allowing the readers that wish to apply these techniques to choose the most suitable definition for his or her case study.

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