A scalable community detection algorithm for large graphs using stochastic block models

Peng, Chengbin; Zhang, Zhihua; Wong, Ka-Chun; Zhang, Xiangliang; Keyes, David E.

doi:10.3233/ida-163156

Cited by 7 publications

(7 citation statements)

References 43 publications

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“…For example, Clauset-Newman-Moore algorithm [8], Fast-unfolding [5], Clique percolation [29], and Community enhanced method [37], are classical heuristicbased approaches, which utilize edge structure to discover network clusters. Besides, Stochastic block models (SBM) [1], [30], [33] are popular Bayesian approaches, which are able to uncover network clusters through maximizing the posterior probability in terms of edge density within predefined blocks (clusters). Community through directed affiliations (CoDA) [47], Deep autoencoder-like nonnegative matrix factorization (DANMF) [48], and Discrete nonnegative matrix factorization (DNMF) [49] are popular models which are based on nonnegative matrix factorization (NMF).…”

Section: Related Workmentioning

confidence: 99%

“…They can be categorized into two classes. Ncut [35], SBM [30], CoDA [47], DANMF [48], and DNMF [49] are five effective methods utilizing network topology to uncover clusters. MVSC [19], CESNA [46], VGAE [18], SCI [44], and ASCD [34] are state-of-the-art approaches to network clustering, which utilize both network structure and vertex features to unfold clusters in the network.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Compared with heuristic ones, model-based methods for network clustering may perform the task by optimizing the formulated objective functions, which quantify the holistic quality of vertex segmentation in terms of edge density, and other additional information, e.g., vertex features. Many machine learning techniques, e.g., spectral analysis [35], [45], matrix factorization [15], [43], [44], Bayesian modeling [7], [30], and deep learning [6], [18], [25], [48], have been successfully adapted to perform the task of uncovering network clusters, taking into consideration the edge structure, vertex features, or both.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vicinal Vertex Allocation for Matrix Factorization in Networks

Bai

Ong

2022

IEEE Trans. Cybern.

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Section: Related Workmentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vicinal Vertex Allocation for Matrix Factorization in Networks

Bai

Ong

2022

IEEE Trans. Cybern.

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“…Snijders [5] first present method of revealing such a cluster structure using posteriori information. The approach named ML-SBM [11] is to use SBM to develop a scalable non-overlapping community detection method on large graphs, which simply based on multi-stage MLE approach to learn latent parameters.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Blockmodels Meets Overlapping Community Detection

Zhao

et al. 2020

IFIP Advances in Information and Communication Technology

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It turns out that the Stochastic Blockmodel (SBM) and its variants can successfully accomplish a variety of tasks, such as discovering community structures. Note that the main limitations are inferencing high time complexity and poor scalability. Our effort is motivated by the goal of harnessing their complementary strengths to develop a scalability SBM for graphs, that also enjoys an efficient inference process and discovery interpretable communities. Unlike traditional SBM that each node is assumed to belong to just one block, we wish to use the node importance to also infer the community membership(s) of each node (as it is one of the goals of SBMs). To this end, we propose a multi-stage maximum likelihood strategy for inferring the latent parameters of adapting the Stochastic Blockmodels to Overlapping Community Detection (OCD-SBM). The intuitive properties to build the model, is more in line with the real-world network to reveal the hidden community structural characteristics. Particularly, this enables inference of not just the node's membership into communities, but the strength of the membership in each of the communities the node belongs to. Experiments conducted on various datasets verify the effectiveness of our model.

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“…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.

show abstract

A scalable community detection algorithm for large graphs using stochastic block models

Cited by 7 publications

References 43 publications

Vicinal Vertex Allocation for Matrix Factorization in Networks

Vicinal Vertex Allocation for Matrix Factorization in Networks

Stochastic Blockmodels Meets Overlapping Community Detection

Adapting Stochastic Block Models to Power-Law Degree Distributions

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