The Multiplex Dependency Structure of Financial Markets

We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex datasets. In particular, we consider multiplex networks made of four layers corresponding, respectively, to linear, nonlinear, tail, and partial correlations among a set of financial time series. We construct the sparse graph on each layer using a standard network filtering procedure, and we then analyse the structural properties of the obtained multiplex networks. The study of the time evolution of the multiplex constructed from financial data uncovers important changes in intrinsically multiplex properties of the network, and such changes are associated with periods of financial stress. We observe that some features are unique to the multiplex structure and would not be visible otherwise by the separate analysis of the single-layer networks corresponding to each dependency measure.

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Multiplex Networks of the Guarantee Market: Evidence from China

Complexity ◽

10.1155/2017/9781890 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Shouwei Li ◽

Shihang Wen

Keyword(s):

Betweenness Centrality ◽

Single Layer ◽

Eigenvector Centrality ◽

Scale Free ◽

Multiplex Networks ◽

Multiplex Network ◽

Different Types ◽

Single Layer Network

We investigate a multiplex network of the guarantee market with three layers corresponding to different types of guarantee relationships in China. We find that three single-layer networks all have the scale-free property and are of disassortative nature. A single-layer network is not quite representative of another single-layer network. The result of the betweenness centrality shows that central companies in one layer are not necessarily central in another layer. And the eigenvector centrality has the same result of the betweenness centrality except the top central company.

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MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

Scientific Reports ◽

10.1038/s41598-021-87987-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Léo Pio-Lopez ◽

Alberto Valdeolivas ◽

Laurent Tichit ◽

Élisabeth Remy ◽

Anaïs Baudot

Keyword(s):

Heterogeneous Networks ◽

Heterogeneous Network ◽

Link Prediction ◽

Network Embedding ◽

Multiplex Networks ◽

Multiplex Network ◽

Gene Associations ◽

Different Types ◽

Embedding Methods ◽

Node Embeddings

AbstractNetwork embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

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Link prediction via layer relevance of multiplex networks

International Journal of Modern Physics C ◽

10.1142/s0129183117501017 ◽

2017 ◽

Vol 28 (08) ◽

pp. 1750101 ◽

Cited By ~ 7

Author(s):

Yabing Yao ◽

Ruisheng Zhang ◽

Fan Yang ◽

Yongna Yuan ◽

Qingshuang Sun ◽

...

Keyword(s):

Structural Properties ◽

Link Prediction ◽

Structural Information ◽

Similarity Index ◽

Single Layer ◽

Structural Features ◽

Prediction Performance ◽

Multiplex Networks ◽

Multiplex Network ◽

Node Similarity

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

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Enhancing synchrony in multiplex network due to rewiring frequency

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2019.0460 ◽

2019 ◽

Vol 475 (2230) ◽

pp. 20190460 ◽

Cited By ~ 2

Author(s):

Sarbendu Rakshit ◽

Bidesh K. Bera ◽

Jürgen Kurths ◽

Dibakar Ghosh

Keyword(s):

Numerical Simulations ◽

Local Stability ◽

Lorenz System ◽

Stability Conditions ◽

Network Architectures ◽

Stability Function ◽

Individual Layer ◽

Multiplex Networks ◽

Multiplex Network ◽

Different Types

Most of the previous studies on synchrony in multiplex networks have been investigated using different types of intralayer network architectures which are either static or temporal. Effect of a temporal layer on intralayer synchrony in a multilayered network still remains elusive. In this paper, we discuss intralayer synchrony in a multiplex network consisting of static and temporal layers and how a temporal layer influences other static layers to enhance synchrony simultaneously. We analytically derive local stability conditions for intralayer synchrony based on the master stability function approach. The analytically derived results are illustrated by numerical simulations on up to five-layers multiplex networks with the paradigmatic Lorenz system as the node dynamics in each individual layer.

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Link prediction in real-world multiplex networks via layer reconstruction method

Royal Society Open Science ◽

10.1098/rsos.191928 ◽

2020 ◽

Vol 7 (7) ◽

pp. 191928

Author(s):

Amir Mahdi Abdolhosseini-Qomi ◽

Seyed Hossein Jafari ◽

Amirheckmat Taghizadeh ◽

Naser Yazdani ◽

Masoud Asadpour ◽

...

Keyword(s):

Real World ◽

Link Prediction ◽

Large Body ◽

Single Layer ◽

Structural Features ◽

Prediction Performance ◽

Reconstruction Method ◽

Multiplex Networks ◽

Technological Complex ◽

Different Types

Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed layer reconstruction method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.

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Learning embeddings for multiplex networks using triplet loss

Applied Network Science ◽

10.1007/s41109-019-0242-0 ◽

2019 ◽

Vol 4 (1) ◽

Author(s):

Seyedsaeed Hajiseyedjavadi ◽

Yu-Ru Lin ◽

Konstantinos Pelechrinis

Keyword(s):

Real World ◽

Online Social Network ◽

Single Layer ◽

Interaction Network ◽

Machine Learning Techniques ◽

Collaboration Network ◽

Multiplex Networks ◽

Multiplex Network ◽

Triplet Loss ◽

Low Dimensional

AbstractLearning low-dimensional representations of graphs has facilitated the use of traditional machine learning techniques to solving classic network analysis tasks such as link prediction, node classification, community detection, etc. However, to date, the vast majority of these learning tasks are focused on traditional single-layer/unimodal networks and largely ignore the case of multiplex networks. A multiplex network is a suitable structure to model multi-dimensional real-world complex systems. It consists of multiple layers where each layer represents a different relationship among the network nodes. In this work, we propose MUNEM, a novel approach for learning a low-dimensional representation of a multiplex network using a triplet loss objective function. In our approach, we preserve the global structure of each layer, while at the same time fusing knowledge among different layers during the learning process. We evaluate the effectiveness of our proposed method by testing and comparing on real-world multiplex networks from different domains, such as collaboration network, protein-protein interaction network, online social network. Finally, in order to deliberately examine the effect of our model’s parameters we conduct extensive experiments on synthetic multiplex networks.

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Inverse Percolation to Quantify Robustness in Multiplex Networks

Complexity ◽

10.1155/2020/8796360 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Edwin Montes-Orozco ◽

Roman-Anselmo Mora-Gutiérrez ◽

Bibiana Obregón-Quintana ◽

Sergio-G. de-los-Cobos-Silva ◽

Eric A. Rincón-García ◽

...

Keyword(s):

Mathematical Model ◽

Social Networks ◽

Single Layer ◽

Multiplex Networks ◽

Vertex Separator ◽

Multiplex Network ◽

The World ◽

Structural Measure ◽

The Mathematical Model

Inverse percolation is known as the problem of finding the minimum set of nodes whose elimination of their links causes the rupture of the network. Inverse percolation has been widely used in various studies of single-layer networks. However, the use and generalization of multiplex networks have been little considered. In this work, we propose a methodology based on inverse percolation to quantify the robustness of multiplex networks. Specifically, we present a modified version of the mathematical model for the multiplex-vertex separator problem (m-VSP). By solving the m-VSP, we can find nodes that cause the rupture of the mutually connected giant component (MCGC) and the large viable cluster (LVC) when their links are removed from the network. The methodology presented in this work was tested in a set of benchmark networks, and as case study, we present an analysis using a set of multiplex social networks modeled with information about the main characteristics of the best universities in the world and the universities in Mexico. The results show that the methodology presented in this work can work in different models and types of 2- and 3-layer multiplex networks without dividing the entire multiplex network into single-layer as some techniques described in the specific literature. Furthermore, thanks to the fact that the technique does not require the calculation of some structural measure or centrality metric, and it is easy to scale for networks of different sizes.

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Creating context for social influence processes in multiplex networks

Network Science ◽

10.1017/nws.2016.28 ◽

2017 ◽

Vol 5 (1) ◽

pp. 1-29 ◽

Cited By ~ 4

Author(s):

J. ANTONIO RIVERO OSTOIC

Keyword(s):

Social Influence ◽

Network Structures ◽

Community Network ◽

Data Set ◽

Multiplex Networks ◽

Multiplex Network ◽

Different Types ◽

Mutual Communication ◽

Formal Business

AbstractThis paper elaborates on two theories of social influence processes to multiplex network structures. First, cohesion influence is based on mutual communication made by different types of relations, and second comparison influence that is built on contrasting types of tie. While a system of bundles with a mutual character constitutes the setting for a multiplex network exposure measure within cohesion, comparison influence is defined algebraically through classes of actors in terms of a weakly balanced semiring structure that considers positive, negative, and also ambivalent types of tie. A case study with these approaches is made on an entrepreneurial community network with formal business relations, informal friendship ties, and perceived competition among the firms, and the methods are validated with the Sampson Monastery data set.

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Nonlinear Structural Fusion for Multiplex Network

Complexity ◽

10.1155/2020/7041564 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Nianwen Ning ◽

Feiyu Long ◽

Chunchun Wang ◽

Youjie Zhang ◽

Yilin Yang ◽

...

Keyword(s):

Network Analysis ◽

Real World ◽

Structural Information ◽

Single Layer ◽

Multiplex Networks ◽

Multiplex Network ◽

Low Weight ◽

Fusion Methods ◽

Low Dimensional ◽

Single Layer Network

Many real-world complex systems have multiple types of relations between their components, and they are popularly modeled as multiplex networks with each type of relation as one layer. Since the fusion analysis of multiplex networks can provide a comprehensive insight, the structural information fusion of multiplex networks has become a crucial issue. However, most of these existing data fusion methods are inappropriate for researchers to apply to complex network analysis directly. The feature-based fusion methods ignore the sharing and complementarity of interlayer structural information. To tackle this problem, we propose a multiplex network structural fusion (MNSF) model, which can construct a network with comprehensive information. It is composed of two modules: the network feature extraction (NFE) module and the network structural fusion (NSF) module. (1) In NFE, MNSF first extracts a low-dimensional vector representation of a node from each layer. Then, we construct a node similarity network based on embedding matrices and K-D tree algorithm. (2) In NSF, we present a nonlinear enhanced iterative fusion (EIF) strategy. EIF can strengthen high-weight edges presented in one (i.e., complementary information) or more (i.e., shared information) networks and weaken low-weight edges (i.e., redundant information). The retention of low-weight edges shared by all layers depends on the tightness of connections of their K-order proximity. The usage of higher-order proximity in EIF alleviates the dependence on the quality of node embedding. Besides, the fused network can be easily exploited by traditional single-layer network analysis methods. Experiments on real-world networks demonstrate that MNSF outperforms the state-of-the-art methods in tasks link prediction and shared community detection.

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Community Detection in Multiplex Networks

ACM Computing Surveys ◽

10.1145/3444688 ◽

2021 ◽

Vol 54 (3) ◽

pp. 1-35

Author(s):

Matteo Magnani ◽

Obaida Hanteer ◽

Roberto Interdonato ◽

Luca Rossi ◽

Andrea Tagarelli

Keyword(s):

Community Detection ◽

Experimental Evaluation ◽

Network Models ◽

Ground Truth ◽

Community Structures ◽

Multiplex Networks ◽

Detection Algorithms ◽

Multiplex Network ◽

The Right ◽

Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

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