Mesoscopic structures reveal the network between the layers of multiplex data sets

Iacovacci, Jacopo; Wu, Zhihao; Bianconi, Ginestra

doi:10.1103/physreve.92.042806

Cited by 32 publications

(41 citation statements)

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“…The combination of citation network modeling by Stochastic Block Modeling with topic modeling was studied for scientific papers by [35], outperforming previous link prediction algorithms. [36] provide a method to compare macroscopic structures of the different layers in a multilayer network that could be applied as a refinement of the overlap, modularity and statistical modeling studied in this paper. Furthermore, is has recently been shown that measures of multilayer network projections induce a significant loss of information compared to the generalized corresponding measure [37], which confirms the relevance of such development that we left for further research.…”

Section: Potential Refinements Of the Methodsmentioning

confidence: 99%

Classifying Patents Based on Their Semantic Content

2018

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In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

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Section: Potential Refinements Of the Methodsmentioning

confidence: 99%

Classifying Patents Based on Their Semantic Content

2018

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“…Finally also the community structure of networks in different layer might be correlated, with communities defined in different layers overlapping with each other. Inference problems on multiplex networks, including the determination of centrality measures for nodes [31][32][33] and the characterization of the multiplex network mesoscale structure 29,30,[35][36][37][38] , can take advantage of these correlations to extract more information from these datasets. In this way a clear path is defined for extracting relevant information from multiplex network structures, which cannot be unveiled by analyzing its single layers taken in isolation or its aggregated description.…”

Section: Structural Multiplex Measures a Multiplex Networkmentioning

confidence: 99%

“…In fact they are very often characterized by a significant overlap of the links in different layers 5,7,25,26 , by correlations between the degree of the same node in different layers 10,27 , by the heterogeneous activity (presence of a node in a layer) of the nodes in different layers 6,28 and by a significant overlap of the communities in different layers 29,30 . These correlations can be exploited to extract relevant information from multiplex network structures that cannot be inferred by analyzing single layers taken in isolation or the aggregated network where all the interactions are taken at the same level.…”

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confidence: 99%

“…This property is routed in the way these networks evolve 30 and reveal an important mechanism by which multiplex networks encode information about their function. Several works address the problem of detecting the community structure of multiplex networks 29,[35][36][37][38] . Recently a new approach 29 has been proposed to solve the relevant problem of qualifying the similarities between the mesoscale structure of the layers of multiplex networks.…”

mentioning

confidence: 99%

“…Several works address the problem of detecting the community structure of multiplex networks 29,[35][36][37][38] . Recently a new approach 29 has been proposed to solve the relevant problem of qualifying the similarities between the mesoscale structure of the layers of multiplex networks. This new framework is able to extract for any multiplex, the network between its layers weighted by a similarity score of their mesoscale structure.…”

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See 2 more Smart Citations

Extracting information from multiplex networks

Iacovacci

Bianconi

2016

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from Big Data. For these reasons characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ S for describing their mesoscale organization and community structure. As working examples for studying these measures we consider three multiplex network datasets coming for social science.Multilayer networks are formed by nodes connected by links describing interactions with different connotations. When the same set of nodes can be connected by different types of links, the resulting multilayer network, is also called a multiplex network. Most social networks are multiplex, since the same set of people might be connected by different types of social ties or might communicate through different means of communication. As networks are ultimately a way to encode information about a complex set of interactions one of the most pressing and challenging problem in network science is to extract relevant information from them. Here we show evidence that the Multiplex PageRank algorithm and the recently introduced indicator function Θ S are able to extract from multiplex networks an information than cannot be inferred by analyzing the single layers taken in isolation or the aggregated network in which links of different type are not distinguished.The vast majority of complex interacting systems, from social networks to the brain and the biological networks of the cell, are multilayer networks 1-3 . Multilayer networks are formed by several networks (layers) describing interactions of different nature and connotation. Therefore multilayer networks encode significant more information than the network which include all the interactions of the multilayer network but does not distinguishes between the different nature of the links. As a consequence of this, one the most pressing challenge in multiplex network theory is devising algorithms and numerical methods to extract relevant information from these network structures. a) Electronic mail: j.iacovacci@qmul.ac.uk b) Electronic mail: g.bianconi@qmul.ac.uk Multilayer networks can be distinguished in two wide classes: multiplex networks and networks of networks 1,3 . Network of networks are multilayer networks formed by layers constituted by different nodes. Examples of network of networks are comple...

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Measuring Author Similarity by Topic Networks

Hsiao

Chen

2022

Proceedings of the Association for Information Science and Tech

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Researchers have applied multiple methods, like co‐authorship and author co‐citation, to measure the relationships between authors and author similarity. These methods play a critical role in various applications of informetrics. A more delicate approach to gauging author similarity improves the related applications. By applying the method developed by the multiplex network, this study investigated how to identify author similarity by the mesoscale structures of author topic networks, which represent an author's research topic by organizing the interactions of the topic terms of their publications. Compared with the co‐authorship relationship and topic‐term similarity results, identifying author similarity by the mesoscale structure of author topic networks provides a better result reflecting how close authors’ research topics are.

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Mesoscopic structures reveal the network between the layers of multiplex data sets

Cited by 32 publications

References 50 publications

Classifying Patents Based on Their Semantic Content

Classifying Patents Based on Their Semantic Content

Extracting information from multiplex networks

Measuring Author Similarity by Topic Networks

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