scholarly journals Information Spreading on Weighted Multiplex Social Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Xuzhen Zhu ◽  
Jinming Ma ◽  
Xin Su ◽  
Hui Tian ◽  
Wei Wang ◽  
...  
Keyword(s):  
Weight Distribution ◽  
Growth Pattern ◽  
Transmission Probability ◽  
Edge Weight ◽  
Multiplex Networks ◽  
Fixed Weight ◽  
Multiplex Network ◽  
Theoretical Predictions ◽  
Information Spreading ◽  
Initial Seed

Information spreading on multiplex networks has been investigated widely. For multiplex networks, the relations of each layer possess different extents of intimacy, which can be described as weighted multiplex networks. Nevertheless, the effect of weighted multiplex network structures on information spreading has not been analyzed comprehensively. We herein propose an information spreading model on a weighted multiplex network. Then, we develop an edge-weight-based compartmental theory to describe the spreading dynamics. We discover that under any adoption threshold of two subnetworks, reducing weight distribution heterogeneity does not alter the growth pattern of the final adoption size versus information transmission probability while accelerating information spreading. For fixed weight distribution, the growth pattern changes with the heterogeneous of degree distribution. There is a critical initial seed size, below which no global information outbreak can occur. Extensive numerical simulations affirm that the theoretical predictions agree well with the numerical results.

scholarly journals Information Spreading on Activity-Driven Temporal Networks with Two-Step Memory

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Linfeng Zhong ◽  
Xiaoyu Xue ◽  
Yu Bai ◽  
Jin Huang ◽  
Qing Cheng ◽  
...  
Keyword(s):  
Network Science ◽  
Critical Mass ◽  
Transmission Probability ◽  
Temporal Network ◽  
Temporal Networks ◽  
Spreading Model ◽  
Spreading Dynamics ◽  
Information Spreading ◽  
Initial Seed ◽  
Markovian Approach

Information spreading dynamics on the temporal network is a hot topic in the field of network science. In this paper, we propose an information spreading model on an activity-driven temporal network, in which a node is accepting the information dependents on the cumulatively received pieces of information in its recent two steps. With a generalized Markovian approach, we analyzed the information spreading size, and revealed that network temporality might suppress or promote the information spreading, which is determined by the information transmission probability. Besides, the system exists a critical mass, below which the information cannot globally outbreak, and above which the information outbreak size does not change with the initial seed size. Our theory can qualitatively well predict the numerical simulations.

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.

scholarly journals MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

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.

THE GROWTH AND DISTRIBUTION OF MUSCLE IN BULLS AND HEIFERS OF TWO BREEDS

1980 ◽  
Vol 60 (3) ◽  
pp. 669-675 ◽  
Author(s):  
S. D. M. JONES ◽  
M. A. PRICE ◽  
R. T. BERG
Keyword(s):  
Weight Distribution ◽  
Growth Pattern ◽  
Muscle Growth ◽  
Allometric Equation ◽  
Muscle Weight ◽  
Growth Coefficient ◽  
Breed Type ◽  
Breed Differences

A trial is reported comparing muscle growth and distribution in 12 bulls and 12 heifers of each of two breed-types: Hereford (HE) and Dairy Synthetic (DY). Serial slaughter was carried out from weaning (163 ± 15.1 days) to approximately 15 mo of age. After slaughter, the left side of each carcass was broken into quarters and then eight wholesale cuts, which were separated into fat, muscle and bone. The growth pattern of muscle in each cut relative to total side muscle was estimated from the growth coefficient, b, in the allometric equation (Y = aXb). Growth coefficients were homogeneous among breeds and sexes, indicating that neither breed nor sex influenced relative muscle growth. Some significant (P < 0.05), though minor, sex and breed differences were found when muscle weight distribution was adjusted to constant side muscle weight. Notably DY heifers had significantly (P < 0.05) more muscle in the high-priced cuts (sum of round, sirloin, loin and rib) than either HE heifers or bulls of either breed-type. When muscle weight was adjusted to constant side weight, bulls were found to have a greater weight of muscle in the high-priced cuts than heifers, and DY animals to have more than HE animals.

Link prediction via layer relevance of multiplex networks

2017 ◽  
Vol 28 (08) ◽  
pp. 1750101 ◽  
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.

scholarly journals Crawling the Community Structure of Multiplex Networks

2019 ◽  
Vol 33 ◽  
pp. 168-175 ◽  
Author(s):  
Ricky Laishram ◽  
Jeremy D. Wendt ◽  
Sucheta Soundarajan
Keyword(s):  
Community Structure ◽  
Real World ◽  
Multiplex Networks ◽  
Data Collector ◽  
Multiplex Network ◽  
Multiple Levels ◽  
Novel Algorithm ◽  
Collection Costs

We examine the problem of crawling the community structure of a multiplex network containing multiple layers of edge relationships. While there has been a great deal of work examining community structure in general, and some work on the problem of sampling a network to preserve its community structure, to the best of our knowledge, this is the first work to consider this problem on multiplex networks. We consider the specific case in which the layers of a multiplex network have different query (collection) costs and reliabilities; and a data collector is interested in identifying the community structure of the most expensive layer. We propose MultiComSample (MCS), a novel algorithm for crawling a multiplex network. MCS uses multiple levels of multi-armed bandits to determine the best layers, communities and node roles for selecting nodes to query. We test MCS against six baseline algorithms on real-world multiplex networks, and achieved large gains in performance. For example, after consuming a budget equivalent to sampling 20% of the nodes in the expensive layer, we observe that MCS outperforms the best baseline by up to 49%.

scholarly journals Enhancing synchrony in multiplex network due to rewiring frequency

2019 ◽  
Vol 475 (2230) ◽  
pp. 20190460 ◽  
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.

Towards optimal edge weight distribution and construction of field-compatible low-density parity-check codes over GF(q)

2014 ◽  
Vol 8 (18) ◽  
pp. 3215-3222 ◽  
Author(s):  
Guojun Han ◽  
Kui Cai ◽  
Yong Liang Guan ◽  
Lingjun Kong ◽  
Kheong Sann Chan
Keyword(s):  
Weight Distribution ◽  
Edge Weight ◽  
Low Density ◽  
Parity Check ◽  
Low Density Parity Check ◽  
Parity Check Codes

scholarly journals The Multiplex Dependency Structure of Financial Markets

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Nicolò Musmeci ◽  
Vincenzo Nicosia ◽  
Tomaso Aste ◽  
Tiziana Di Matteo ◽  
Vito Latora
Keyword(s):  
Financial Markets ◽  
Financial Time Series ◽  
Single Layer ◽  
Separate Analysis ◽  
Dependency Structure ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Different Types ◽  
Partial Correlations ◽  
Filtering Procedure

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.

scholarly journals Infinite Families of 2-Designs from a Class of Linear Codes Related to Dembowski-Ostrom Functions

2021 ◽  
pp. 1-15
Author(s):  
Rong Wang ◽  
Xiaoni Du ◽  
Cuiling Fan ◽  
Zhihua Niu
Keyword(s):  
Finite Field ◽  
Weight Distribution ◽  
Coding Theory ◽  
Exponential Sums ◽  
Linear Codes ◽  
Cyclic Codes ◽  
Research Interest ◽  
Combinatorial Formula ◽  
Text Design ◽  
Fixed Weight

Due to their important applications to coding theory, cryptography, communications and statistics, combinatorial [Formula: see text]-designs have attracted lots of research interest for decades. The interplay between coding theory and [Formula: see text]-designs started many years ago. It is generally known that [Formula: see text]-designs can be used to derive linear codes over any finite field, and that the supports of all codewords with a fixed weight in a code also may hold a [Formula: see text]-design. In this paper, we first construct a class of linear codes from cyclic codes related to Dembowski-Ostrom functions. By using exponential sums, we then determine the weight distribution of the linear codes. Finally, we obtain infinite families of [Formula: see text]-designs from the supports of all codewords with a fixed weight in these codes. Furthermore, the parameters of [Formula: see text]-designs are calculated explicitly.

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