Multiplex networks with heterogeneous activities of the nodes

Cellai, Davide; Bianconi, Ginestra

doi:10.1103/physreve.93.032302

Cited by 29 publications

(28 citation statements)

References 42 publications

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“…On one side we will interpret the multiplex network as a colored network where different types of links are assigned a different influence. On the other side we will exploit the weighted, directed bipartite network formed by nodes and layers [11,21] that can be constructed from the multiplex network. This bipartite network will provide us with information about the activity of the nodes [22] in each layer, i.e.…”

Section: A Multirank: the Motivationmentioning

confidence: 99%

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Centralities of nodes and influences of layers in large multiplex networks

Rahmede

Iacovacci

Arenas

et al. 2017

Journal of Complex Networks

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We formulate and propose an algorithm (MultiRank) for the ranking of nodes and layers in large multiplex networks. MultiRank takes into account the full multiplex network structure of the data and exploits the dual nature of the network in terms of nodes and layers. The proposed centrality of the layers (influences) and the centrality of the nodes are determined by a coupled set of equations. The basic idea consists in assigning more centrality to nodes that receive links from highly influential layers and from already central nodes. The layers are more influential if highly central nodes are active in them. The algorithm applies to directed/undirected as well as to weighted/unweighted multiplex networks. We discuss the application of MultiRank to three major examples of multiplex network datasets: the European Air Transportation Multiplex Network, the Pierre Auger Multiplex Collaboration Network and the FAO Multiplex Trade Network.

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Section: A Multirank: the Motivationmentioning

confidence: 99%

“…For undirected multiplex networks the matrices B in and B out are identical. Note that if node i is not connected in layer α we have B in αi = B out αi = 0 and we say that the node is inactive in layer α [21,22]. Otherwise, if node i is connected in layer α, we will say that the node is active in layer α.…”

Section: A Multirank: the Motivationmentioning

confidence: 99%

Centralities of nodes and influences of layers in large multiplex networks

Rahmede

Iacovacci

Arenas

et al. 2017

Journal of Complex Networks

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“…We refer the interested reader to Refs. [10,26,32] where this case and its implications for the percolation transition, including asymptotic behavior in case of a large number of layers, are discussed in detail.…”

Section: A Multiplex Networkmentioning

confidence: 99%

“…The emergence of the MCGC has been studied on a variety of multilayer structures including multiplex networks [11][12][13][28][29][30][31][32] and networks of networks [33][34][35]. Networks of networks are multilayer networks formed by different networks (layers), where the nodes of different networks might be related by interdependencies.…”

Section: Introductionmentioning

confidence: 99%

Message passing theory for percolation models on multiplex networks with link overlap

2016

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Multiplex networks describe a large variety of complex systems, including infrastructures, transportation networks, and biological systems. Most of these networks feature a significant link overlap. It is therefore of particular importance to characterize the mutually connected giant component in these networks. Here we provide a message passing theory for characterizing the percolation transition in multiplex networks with link overlap and an arbitrary number of layers M. Specifically we propose and compare two message passing algorithms that generalize the algorithm widely used to study the percolation transition in multiplex networks without link overlap. The first algorithm describes a directed percolation transition and admits an epidemic spreading interpretation. The second algorithm describes the emergence of the mutually connected giant component, that is the percolation transition, but does not preserve the epidemic spreading interpretation. We obtain the phase diagrams for the percolation and directed percolation transition in simple representative cases. We demonstrate that for the same multiplex network structure, in which the directed percolation transition has nontrivial tricritical points, the percolation transition has a discontinuous phase transition, with the exception of the trivial case in which all the layers completely overlap.

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“…The pruning of these type of networks and separation of significant edges from non-significant ones is not a trivial task [36,37,44]. For this reasons we start from bipartite network and use method that determines the significance of W ij link based on configuration model of random bipartite networks [36,37,45,46]. In this model of random networks the event size and the number of events a member attended are fixed, while all other correlations are destroyed (see SI for further explanations).…”

Section: Network Construction and Filteringmentioning

confidence: 99%

Associative nature of event participation dynamics: A network theory approach

Smiljanić

Dankulov

2017

PLoS ONE

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The affiliation with various social groups can be a critical factor when it comes to quality of life of each individual, making such groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group’s ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist’s engagement in conference activities of the specific scientific community depends on the balance between the numbers of previous attendances and non-attendances and is directly related to scientist’s association with that community. Here we show that the same holds for leisure groups of the Meetup website and further quantify individual members’ association with the group. We examine how structure of personal social networks is evolving with the event attendance. Our results show that member’s increasing engagement in the group activities is primarily associated with the strengthening of already existing ties and increase in the bonding social capital. We also show that Meetup social networks mostly grow trough big events, while small events contribute to the groups cohesiveness.

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Multiplex networks with heterogeneous activities of the nodes

Cited by 29 publications

References 42 publications

Centralities of nodes and influences of layers in large multiplex networks

Centralities of nodes and influences of layers in large multiplex networks

Message passing theory for percolation models on multiplex networks with link overlap

Associative nature of event participation dynamics: A network theory approach

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