Dynamic Infinite Mixed-Membership Stochastic Blockmodel

Cao, Longbing; Xu, Richard Yi Da

doi:10.1109/tnnls.2014.2369374

Cited by 22 publications

(23 citation statements)

References 27 publications

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“…In this way, K becomes a random quantity generated by the process, and potentially an infinite number of groups is allowed. By incorporating the Dirichlet process in the SBM, as did Kurihara et al (2006), Mørup et al (2011), Yang (2011, 2014), Kim et al (2013) andFan et al (2015), K can be estimated along other parameters and latent variables.…”

Section: Modellingmentioning

confidence: 99%

“…Another example is that a soft clustering approach influences partly how K is being modelled or selected. The models considered here either opted for modelling by a Dirichlet process (Kurihara et al, 2006, Kim et al, 2013, Fan et al, 2015 or selecting by a criterion (Airoldi et al, 2008, Fu et al, 2009, Gopalan et al, 2012, Li et al, 2016. Also, the added computational complexity prompted Li et al (2016) to propose an inference algorithm which exploits the sparity of graphs.…”

Section: Approachesmentioning

confidence: 99%

“…While Fan et al (2015) also applied their MTI model to the Enron dataset, they only reported that it outperformed the then existing models such as the MMSBM (Airoldi et al, 2008)…”

Section: Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

A review of stochastic block models and extensions for graph clustering

Lee¹,

Wilkinson²

2019

Appl Netw Sci

157

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There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated. We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data. How different approaches cope with various issues will be summarised and compared, to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature.

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Section: Modellingmentioning

confidence: 99%

Section: Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A review of stochastic block models and extensions for graph clustering

Lee¹,

Wilkinson²

2019

Appl Netw Sci

157

View full text Add to dashboard Cite

show abstract

“…Other main extension of SBM includes hierarchical SBM [14], integrating node attributes into SBM [15], dynamic infinite extension of MMSBM [16], and improving model scalability by stochastic variational methods [17] [18]. Due to its computational flexibility and structural interpretation, SBM and its extension have been popularizing in a variety of network analysis tasks, e.g., uncovering social groups from relationship data [19] [20][21] [22], functional annotation of protein-protein interaction networks [13], and network clustering [23].…”

Section: Introductionmentioning

confidence: 99%

Adapting Stochastic Block Models to Power-Law Degree Distributions

Qiao

Bian

et al. 2019

IEEE Trans. Cybern.

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Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.

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“…C OMMUNITY detection and network partitioning is an emergent topic in various areas including social-media recommendation [1], customer partitioning [2], [3], social network analysis [4], [5], and partitioning protein interaction network tasks [6]- [10]. Many models have been proposed in recent years to address this problem by using link data (e.g., a person's view toward others).…”

Section: Introductionmentioning

confidence: 99%

Learning Nonparametric Relational Models by Conjugately Incorporating Node Information in a Network

Cao

Song

2017

IEEE Trans. Cybern.

Self Cite

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Relational model learning is useful for numerous practical applications. Many algorithms have been proposed in recent years to tackle this important yet challenging problem. Existing algorithms utilize only binary directional link data to recover hidden network structures. However, there exists far richer and more meaningful information in other parts of a network which one can (and should) exploit. The attributes associated with each node, for instance, contain crucial information to help practitioners understand the underlying relationships in a network. For this reason, in this paper, we propose two models and their solutions, namely the node-information involved mixed-membership model and the node-information involved latent-feature model, in an effort to systematically incorporate additional node information. To effectively achieve this aim, node information is used to generate individual sticks of a stickbreaking process. In this way, not only can we avoid the need to prespecify the number of communities beforehand, the algorithm also encourages that nodes exhibiting similar information have a higher chance of assigning the same community membership. Substantial efforts have been made toward achieving the appropriateness and efficiency of these models, including the use of conjugate priors. We evaluate our framework and its inference algorithms using real-world data sets, which show the generality and effectiveness of our models in capturing implicit network structures.

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Dynamic Infinite Mixed-Membership Stochastic Blockmodel

Cited by 22 publications

References 27 publications

A review of stochastic block models and extensions for graph clustering

A review of stochastic block models and extensions for graph clustering

Adapting Stochastic Block Models to Power-Law Degree Distributions

Learning Nonparametric Relational Models by Conjugately Incorporating Node Information in a Network

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