Infinite-degree-corrected stochastic block model

Herlau, Tue; Schmidt, Mikkel N.; Mørup, Morten

doi:10.1103/physreve.90.032819

Cited by 20 publications

(22 citation statements)

References 22 publications

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“…Our model integrates the approach of Kemp et al(2006), who use the CRP to detect community structure, and Ghosh et al(2010), who use the DP to induce clustering among the ‘productivity’ and ‘attractiveness’ parameters of a variation of the p1 model (Holland and Leinhardt, 1981) and a social relations model (Gill and Swartz, 2007). The infinite-degree-corrected stochastic block model (Herlau et al, 2014) also uses the CRP for community detection. However, they consider weighted links modelled using a Poisson distribution and they do not consider clustering of the degree parameters.…”

Section: Introductionmentioning

confidence: 99%

Dynamic degree-corrected blockmodels for social networks: A nonparametric approach

Tan

2018

Statistical Modelling

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A nonparametric approach to the modeling of social networks using degreecorrected stochastic blockmodels is proposed. The model for static network consists of a stochastic blockmodel using a probit regression formulation and popularity parameters are incorporated to account for degree heterogeneity. Dirichlet processes are used to detect community structure as well as induce clustering in the popularity parameters. This approach is flexible yet parsimonious as it allows the appropriate number of communities and popularity clusters to be determined automatically by the data. We further discuss some ways of extending the static model to dynamic networks. We consider a Bayesian approach and derive Gibbs samplers for posterior inference. The models are illustrated using several real-world benchmark social networks.

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Section: Introductionmentioning

confidence: 99%

Dynamic degree-corrected blockmodels for social networks: A nonparametric approach

Tan

2018

Statistical Modelling

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“…If no prior knowledge exists, model selection using statistical and information theoretic measures could be employed [14]. Also more complex models exist, which might learn the number of groups from data, as well as vary the number of nodes in synthetic graphs [14], [22], [25].…”

Section: Discussionmentioning

confidence: 99%

Generating synthetic Internet- and IP-topologies using the Stochastic-Block-Model

Kalmbach

Blenk

Kluegel

et al. 2017

2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

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Abstract-Developing models to generate realistic graphs of communication networks often requires a deep understanding and extensive analysis of the underlying network structure. Since deployed communication networks are dynamic, the findings a generator is based on might lose validity. We alleviate the need for extensive analysis of graphs by estimating parameters of a probabilistic model. The model parameters encode the structure of the graph, which is thus learned in an unsupervised fashion. Synthetic graphs can be generated from the model and will have the structure previously inferred. For this, we use the Stochastic-Block-Model (SBM) and the Degree-Corrected-BlockModel (DCBM), a variant allowing for heavy tailed degree distributions. The models originate in the social sciences and separate a graph into groups of nodes. To show the applicability of the models to the task of synthetic graph generation in the domain of communication networks, we use one router level and one IP-to-IP communication graph. We assert the quality of the generated models by evaluating a number of graph features and comparing our results to those obtained with the network generator Orbis. We find our approach to be on par with, or even outperforming Orbis. Furthermore, the models are able to capture large-scale structure in communication networks.

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“…This method is reasonable, as the Poisson distribution is the natural probability distribution for modeling counts. Tue Herlau et al [18] formulated a nonparametric Bayesian generative model for the DCSBM (they named it IDCSBM), where the number of communities is inferred via the Chinese restaurant process [19]. These two models can be used to detect only nonoverlapping communities.…”

Section: Related Workmentioning

confidence: 99%

Overlapping community detection for count-value networks

Wang

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionCommunity detection is a fundamental problem in network analysis, as community structure which almost exists in all networks, is the most widely studied structural properties of networks. Statistical network generative model, due to its solid theoretical base, remarkable interpretability and relative tractability, has been wildly used for community detecting tasks [1]. Existing network generative models can be grouped into two classes: the latent class model, and the latent feature model. The latent class model assume that each individual only affiliate with a single class (as show in Fig. 1a). The latent feature model, increases the flexibility of the generative process by permitting each object possesses a vector of features and determine the link probabilities based on interactions among the features. In many real-world networks, communities are ordinarily overlapping rather than disjoint, so assuming that each object having hard membership in only one cluster became too restrict to consistent with the facts.An important challenge in community detection is to specify the number of communities in advance, as we do not have good prior knowledge of how many parameters the model requires to explain the data well. The relational infinite latent feature model AbstractDetecting network overlapping community has become a very hot research topic in the literature. However, overlapping community detection for count-value networks that naturally arise and are pervasive in our modern life, has not yet been thoroughly studied. We propose a generative model for count-value networks with overlapping community structure and use the Indian buffet process to model the community assignment matrix Z; thus, provide a flexible nonparametric Bayesian scheme that can allow the number of communities K to increase as more and more data are encountered instead of to be fixed in advance. Both collapsed and uncollapsed Gibbs sampler for the generative model have been derived. We conduct extensive experiments on simulated network data and real network data, and estimate the inference quality on single variable parameters. We find that the proposed model and inference procedure can bring us the desired experimental results. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Infinite-degree-corrected stochastic block model

Cited by 20 publications

References 22 publications

Dynamic degree-corrected blockmodels for social networks: A nonparametric approach

Dynamic degree-corrected blockmodels for social networks: A nonparametric approach

Generating synthetic Internet- and IP-topologies using the Stochastic-Block-Model

Overlapping community detection for count-value networks

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