Cross-layers cascade in multiplex networks

Li, Zhaofeng; Yan, Fuhan; Jiang, Yichuan

doi:10.1007/s10458-015-9305-5

Cited by 23 publications

(19 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a consequence, the number of "infected"nodes changes with time. The percentage of infected nodes (i.e., infection size) at a specific time is sometimes called epidemic dynamics [79]; the cascade velocity measures how fast an item (e.g., a message) reaches some relevant nodes or a given number of nodes in a cascade [49]. Finally, we can study the survival probability, which is the probability that an infection, started from a single node, is still active at time t [46].…”

Section: Preliminariesmentioning

confidence: 99%

“…However, some empirical studies point out that different link types [119], [120], [121] and topologically inefficient paths [122] may actually decrease the spreading speed in monoplex networks; this suggests that this area of research needs more attention. Recently [49] addressed the velocity of the cascade process in multiplex networks by considering the role of inter-layer links. In this simulation-based study, the authors find that the obstruction of an inter-layer link connecting the shortest paths distributed in multiple layers leads to a slower spreading process in multiplex networks.…”

Section: Diffusion Velocitymentioning

confidence: 99%

See 1 more Smart Citation

Spreading Processes in Multilayer Networks

Salehi

Sharma

Marzolla

et al. 2015

IEEE Trans. Netw. Sci. Eng.

212

150

View full text Add to dashboard Cite

Abstract-Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.

show abstract

Section: Preliminariesmentioning

confidence: 99%

Section: Diffusion Velocitymentioning

confidence: 99%

Spreading Processes in Multilayer Networks

Salehi

Sharma

Marzolla

et al. 2015

IEEE Trans. Netw. Sci. Eng.

212

150

View full text Add to dashboard Cite

show abstract

“…The evolution of cooperation via evolutionary game dynamics on multiplex networks is studied in [42]. Jiang and Li [43] study information cascade in multiplex networks. Sergey et al [44] present an analytical solution for the catastrophic cascade of failures in interdependent networks.…”

Section: Other Important Aspects Of Multiplex Network: Dementioning

confidence: 99%

Cross-layer betweenness centrality in multiplex networks with applications

Chakraborty

Narayanam

2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

Several real-life social systems witness the presence of multiple interaction types (or layers) among the entities, thus establishing a collection of co-evolving networks, known as multiplex networks. More recently, there has been a significant interest in developing certain centrality measures in multiplex networks to understand the influential power of the entities (to be referred as vertices or nodes hereafter). In this paper, we consider the problem of studying how frequently the nodes occur on the shortest paths between other nodes in the multiplex networks. As opposed to simplex networks, the shortest paths between nodes can possibly traverse through multiple layers in multiplex networks. Motivated by this phenomenon, we propose a new metric to address the above problem and we call this new metric cross-layer betweenness centrality (CBC).Our definition of CBC measure takes into account the interplay among multiple layers in determining the shortest paths in multiplex networks. We propose an efficient algorithm to compute CBC and show that it runs much faster than the naïve computation of this measure. We show the efficacy of the proposed algorithm using thorough experimentation on two realworld multiplex networks. We further demonstrate the practical utility of CBC by applying it in the following three application contexts: discovering non-overlapping community structure in multiplex networks, identifying interdisciplinary researchers from a multiplex co-authorship network, and the initiator selection for message spreading. In all these application scenarios, the respective solution methods based on the proposed CBC are found to be significantly better performing than that of the corresponding benchmark approaches.

show abstract

“…In related studies on diffusion in multiplex networks, a common method is extending the existing diffusion models in simplex networks by considering the multiplicity of links [Jiang and Jiang 2015;Li and Jiang 2014]. Two typical approaches are generalizing the epidemic model and the threshold model in simplex networks to multiplex networks.…”

Section: Diffusion Processes In Multiplex Networkmentioning

confidence: 99%

Reliable Task Allocation with Load Balancing in Multiplex Networks

Jiang

Zhou

Liu

2015

ACM Trans. Auton. Adapt. Syst.

Self Cite

View full text Add to dashboard Cite

In multiplex networks, agents are connected by multiple types of links; a multiplex network can be split into more than one network layer that is composed of the same type of links and involved agents. Each network link type has a bias for communicating different types of resources; thus, the task's access to the required resources in multiplex networks is strongly related to the network link types. However, traditional task allocation and load balancing methods only considered the situations of agents themselves and did not address the effects of network link types in multiplex networks. To solve this problem, this article considers both link types and agents, and substantially extends the existing work by highlighting the effect of network layers on task allocation and load balancing. Two multiplex network-adapted models of task allocation with load balancing are presented: network layer-oriented allocation and agent-oriented allocation. This article also addresses the unreliability in multiplex networks, which includes the unreliable links and agents, and implements a reliable task allocation based on a negotiation reputation and reward mechanism. Our findings show that both of our presented models can effectively and robustly satisfy the task allocation objectives in unreliable multiplex networks; the experiments prove that they can significantly reduce the time costs and improve the success rate of tasks for multiplex networks over the traditional simplex network-adapted task allocation model. Lastly, we find that our presented network layer-oriented allocation performs much better in terms of reliability and allocation time compared to our presented agent-oriented allocation, which further explains the importance of network layers in multiplex networks.

show abstract

Cross-layers cascade in multiplex networks

Cited by 23 publications

References 51 publications

Spreading Processes in Multilayer Networks

Spreading Processes in Multilayer Networks

Cross-layer betweenness centrality in multiplex networks with applications

Reliable Task Allocation with Load Balancing in Multiplex Networks

Contact Info

Product

Resources

About