Tensorial graph learning for link prediction in generalized heterogeneous networks

Chen, Zhenyu; Fan, Zhi‐Ping; Sun, Minghe

doi:10.1016/j.ejor.2020.05.062

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…In this context, the concept of heterogeneous networks has received increasing attention in the literature. These kinds of networks are currently classified in different ways, including heterogeneous information networks (Gupta & Kumar (2020); Chen et al (2021)), multilayer or multiplex networks (De Domenico et al (2013); Kivelä et al (2014); Boccaletti et al (2014)) and multidimensional networks (Berlingerio et al (2013)).…”

Section: Introductionmentioning

confidence: 99%

Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network

Bartesaghi¹,

Clemente²,

Grassi³

2022

Preprint

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In this paper, we propose new definitions of clustering coefficient for weighted and directed multiplex networks. We extend in the multiplex framework the formulas of clustering coefficients existing in the literature for weighted directed monoplex networks. We quantify how deep a node is involved in a cohesive structure focusing on a single layer, all layers or the entire system, respectively. The coefficients capture various features intrinsically related to the complex topology of the multiplex network. We test their effectiveness applying them to a particularly complex structure such as the international trade network. The trade data integrate different aspects and they can be described by a directed and weighted multiplex network, where each layer represents import and export relationships between countries for a given sector. The proposed coefficients find successful application in describing the interrelations of the trade network, allowing to disentangle the effects of countries and sectors and jointly consider the interactions between them.

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Section: Introductionmentioning

confidence: 99%

Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network

Bartesaghi¹,

Clemente²,

Grassi³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In this context, the concept of heterogeneous networks has received increasing attention in the literature. These kind of networks are currently classified in different ways, including heterogeneous information networks (Gupta & Kumar (2020); Chen et al (2021)), multilayer networks (De Domenico et al (2013); Kivelä et al (2014); Boccaletti et al (2014)) and multidimensional networks (Berlingerio et al (2013)). In this work we refer to these networks as multilayer networks.…”

Section: Introductionmentioning

confidence: 99%

A tensor-based unified approach for clustering coefficients in financial multiplex networks

Bartesaghi,

Clemente,

Grassi

2021

Preprint

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Complex systems in the real world are frequently characterized by entities connected by relationships of different nature. These systems can be appropriately described by multilayer networks where the different relations between nodes can be conveniently expressed structuring the network through different layers. To extend in a multilayer context the classical network indicators proposed for monolayer networks is an important issue. In this work we study the incidence of triangular patterns expressed through the local clustering coefficient in a weighted undirected multilayer network. We provide new local clustering coefficients for multilayer networks, looking at the network from different perspectives depending on the node's position, as well as a global clustering coefficient for the whole network. We also prove that all the well-known expressions for clustering coefficients existing in the literature, suitably extended to the multilayer framework, may be unified into our proposal, both in terms of tensors and supradjacency matrix. To show the effectiveness of the proposed measures, we study an application to the multilayer temporal financial network based on the returns of the S&P100 assets.

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PQKLP: Projected Quantum Kernel based Link Prediction in Dynamic Networks

Kumar

Mishra

Biswas

2022

Computer Communications

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Tensorial graph learning for link prediction in generalized heterogeneous networks

Cited by 6 publications

References 34 publications

Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network

Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network

A tensor-based unified approach for clustering coefficients in financial multiplex networks

PQKLP: Projected Quantum Kernel based Link Prediction in Dynamic Networks

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