Community structure detection in multipartite networks

Gaskó, Noémi; Bota, Florentin; Suciu, Mihai Alexandru; Lung, Rodica Ioana

doi:10.1145/3071178.3071295

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Related works Some extensions of standard descriptive tools—such as community detection—to more complex networks have been proposed in the literature (see for instance Yang and Leskovec, 2012; Gaskó et al, 2017). These metrics or descriptive tools have the drawback of only searching for specific types of structures, contrary to clustering methods based on probabilistic mixture models.…”

Section: Introductionmentioning

confidence: 99%

Block models for generalized multipartite networks: Applications in ecology and ethnobiology

Bar-Hen

Barbillon

Donnet

2020

Statistical Modelling

View full text Add to dashboard Cite

Generalized multipartite networks consist in the joint observation of several networks implying some common pre-specified groups of individuals. Such complex networks arise commonly in social sciences, biology, ecology, etc. We propose a flexible probabilistic model named Multipartite Block Model (MBM) able to unravel the topology of multipartite networks by identifying clusters (blocks) of nodes sharing the same patterns of connectivity across the collection of networks they are involved in. The model parameters are estimated through a variational version of the Expectation–Maximization algorithm. The numbers of blocks are chosen using an Integrated Completed Likelihood criterion specifically designed for our model. A simulation study illustrates the robustness of the inference strategy. Finally, two datasets respectively issued from ecology and ethnobiology are analyzed with the MBM in order to illustrate its flexibility and its relevance for the analysis of real datasets. The inference procedure is implemented in an R -package GREMLIN , available on Github ( https://github.com/Demiperimetre/GREMLINhttps://github.com/Demiperimetre/GREMLIN ).

show abstract

Section: Introductionmentioning

confidence: 99%

Block models for generalized multipartite networks: Applications in ecology and ethnobiology

Bar-Hen

Barbillon

Donnet

2020

Statistical Modelling

View full text Add to dashboard Cite

show abstract

“…Multipartite networks require the development of specific statistical tools. Some extensions of standard descriptive tools -such as community detectionhave been proposed in the literature (see for instance Yang and Leskovec [2012] or Gaskó et al [2017]). When aiming at clustering similar entities based on their connectivity patterns without any a priori hypothesis about the patterns to be found (e.g.…”

Section: Introductionmentioning

confidence: 99%

Block models for multipartite networks.Applications in ecology and ethnobiology

Bar-Hen,

Barbillon,

Donnet

2018

Preprint

View full text Add to dashboard Cite

Modeling relations between individuals is a classical question in social sciences, ecology, etc. In order to uncover a latent structure in the data, a popular approach consists in clustering individuals according to the observed patterns of interactions. To do so, Stochastic Block Models and Latent Block models are standard tools for clustering the individuals with respect to their connections in a unique network. However, when adopting an integrative point of view, individuals are not involved in a unique network only but are part of several networks, resulting in a potentially complex multipartite network. In this paper, we propose a latent variable model which can handle multipartite networks. The latent variables correspond to clusters of individuals which shape their connections in all the networks they are involved in. Our model is then an extension of the latent block model and stochastic block model. The parameters are estimated through a variational Expectation-Maximization procedure. The numbers of blocks are chosen with the Integrated Completed Likelihood criterion, a penalized likelihood criterion. This model is motivated by two datasets issued from ecology and ethnobiology.

show abstract

Community structure detection in multipartite networks

Cited by 2 publications

References 15 publications

Block models for generalized multipartite networks: Applications in ecology and ethnobiology

Block models for generalized multipartite networks: Applications in ecology and ethnobiology

Block models for multipartite networks.Applications in ecology and ethnobiology

Contact Info

Product

Resources

About