# The physics of spreading processes in multilayer networks

@article{Domenico2016ThePO, title={The physics of spreading processes in multilayer networks}, author={Manlio De Domenico and Clara Granell and Mason A. Porter and Alex Arenas}, journal={Nature Physics}, year={2016}, volume={12}, pages={901-906} }

Despite the success of traditional network analysis, standard networks provide a limited representation of complex systems, which often include different types of relationships (or ‘multiplexity’) between their components. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and be a major obstacle towards attempts to understand complex systems. The recent multilayer…

## 390 Citations

Algorithmic complexity of multiplex networks

- Computer ScienceArXiv
- 2019

It is found that there exists a maximum amount of additional information that a multilayer model can encode with respect to the equivalent single-layer graph, and an intuitive way to encode a multi-layer network into a bit string is proposed.

Multiple structural transitions in interacting networks

- Computer SciencePhysical review. E
- 2018

This work reveals multiple structural transitions for the algebraic connectivity of such systems, between regimes in which each network layer keeps its independent identity or drives diffusive processes over the whole system, thus generalizing previous results reporting a single transition point.

Diffusion geometry of multiplex and interdependent systems.

- MathematicsPhysical review. E
- 2021

The multilayer diffusion geometry of synthetic and empirical systems is characterized, highlighting the role played by different random search dynamics in shaping the geometric features of the corresponding diffusion manifolds.

Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks

- Computer Science
- 2021

This work provides insights on the importance of nodes in the multiplex network and gives a feasible framework to investigate inﬂuential spreaders in the asymmetrically coevolving dynamics.

A local perspective on community structure in multilayer networks

- Computer ScienceNetwork Science
- 2017

This work analyzes the local behavior of different random walks on multiplex networks and shows that they have very different bottlenecks, which correspond to rather different notions of what it means for a set of nodes to be a good community.

Sequential seeding in multilayer networks

- Computer ScienceChaos
- 2021

The results show that sequential seeding in multilayer networks outperforms the traditional approach by increasing the coverage and allowing to save the seeding budget, however, it also extends the duration of the spreading process.

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

- Computer ScienceIEEE Access
- 2020

This survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward.

Detecting Hidden Layers from Spreading Dynamics on Complex Networks

- Computer SciencePhysical review. E
- 2021

This paper proposes methods for hidden layer identification and reconstruction and shows that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model.

Analysing Motifs in Multilayer Networks

- Computer ScienceArXiv
- 2019

It is found that multilayer motifs in social networks are more homogeneous across layers, indicating that different types of social relationships are reinforcing each other, while those in the transportation network are more complementary across layers.

Identifying Influential Spreaders in Complex Multilayer Networks: A Centrality Perspective

- Computer ScienceIEEE Transactions on Network Science and Engineering
- 2019

The purpose is to devise a method that can accurately detect nodes able to exert strong influence over the multilayer network, based solely on local knowledge of a network’s topology in order to be fast and scalable due to the huge size of the network, and thus suitable for both real-time applications and offline mining.

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