Social contagions on correlated multiplex networks

2018 ◽  
Vol 499 ◽  
pp. 121-128 ◽  
Author(s):  
Wei Wang ◽  
Meng Cai ◽  
Muhua Zheng
Keyword(s):  
Multiplex Networks

EXPLOSIVE SYNCHRONIZATION OF COMBINATIONAL PHASES ON RANDOM MULTIPLEX NETWORKS

2016 ◽  
Vol 6 (2) ◽  
pp. 99-108 ◽  
Author(s):  
Guanying Huo ◽  
Xin Jiang ◽  
Lili Ma ◽  
Quantong Guo ◽  
Yifang Ma ◽  
...  
Keyword(s):  
Multiplex Networks ◽  
Explosive Synchronization

scholarly journals Spreading of localized attacks on spatial multiplex networks with a community structure

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Dana Vaknin ◽  
Bnaya Gross ◽  
Sergey V. Buldyrev ◽  
Shlomo Havlin
Keyword(s):  
Community Structure ◽  
Multiplex Networks ◽  
Spatial Multiplex

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

scholarly journals A group-based centrality for undirected multiplex networks: a case study of the Brazilian Car Wash Operation

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bruno Cesar Barreto De Figueiredo ◽  
Fabiola Guerra Nakamura ◽  
Eduardo Freire Nakamura
Keyword(s):  
Multiplex Networks ◽  
Car Wash

scholarly journals Community Detection in Multiplex Networks

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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