Models, Topology and Inference of Multilayer and Higher-Order Networks

Dear Colleagues,

In recent years, network science has moved from the study of simple networks to the investigation of more complex representations, including multilayer and higher-order structures. These new representations enable researchers to overcome some of the limitations of simple networks by integrating both heterogeneous and higher-order information; consequently, more information can be extracted from data, and the interplay between network structure and dynamics can be more effectively captured. In particular, new perspectives on information theory and inference on networks, combined with network topology and geometry, enable the development of new inference algorithms that offer new ways of analyzing multilayer and higher-order networks. Moreover, going beyond simple pairwise networks is key to revealing important topological and geometrical aspects of dynamical processes and offers a unique opportunity to better understand dynamics on networks. 

Therefore, we welcome contributions including, but not limited to, the following topics:

Multilayer networks
Higher-order networks

Information theory of  networks:
Applied topology
Higher-order network geometry
Higher-order network representation
Multilayer/hypergraph network embedding
Visualization of higher-order networks
Inference algorithms
Community detection 
Bayesian inference
Higher-order/multilayer centrality measures

Dynamical models/higher-order dynamics:
Diffusion models/random walks
Epidemic spreading/contagion 
Percolation models
Synchronization 
Higher-order cascading failure models
Dynamics in the presence of triadic interactions

Prof. Dr. Ginestra Bianconi
Dr. Rubén J. Sánchez-García
Dr. Anthony Baptista
Guest Editors

Hanlin Sun
Guest Editor Assistant

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