Community detection with nodal information: Likelihood and its variational approximation

Weng, Haolei; Feng, Yang

doi:10.1002/sta4.428

Cited by 15 publications

(4 citation statements)

References 68 publications

(111 reference statements)

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“…Their model is constructed under the assumption that the network structure is generated from a stochastic block model. Similarly, Weng and Feng (2022) proposed a node‐coupled stochastic block model for community detection, which combined both sources of edge and nodal information. Apart from stochastic block models, the latent space model is also an effective tool for network modelling.…”

Section: Introductionmentioning

confidence: 99%

Community detection in attributed collaboration network for statisticians

Zhang

Pan

Wang

et al. 2023

Stat

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Scientific collaboration helps to promote the dissemination of knowledge and is essential in breeding innovation. Collaboration network analysis is a useful tool to study researchers' collaborations. In this work, we collect papers published between 2001 and 2018 in 43 statistical journals and investigate the collaborative trends and patterns. We find that more and more researchers take part in statistical research, and cooperation among them is strengthening. We further construct an attributed collaboration network and extract its core. Community detection is conducted on the core network by using the edge cross‐validation (ECV) method and the attributed network clustering algorithm (ANCA). In particular, we extend the ANCA to deal with networks having both categorical and continuous attributes. Influential researchers are identified in each community. Furthermore, two kinds of homophily are revealed in our collaboration network: research topic homophily and spatial proximity homophily. Based on the homophily and transitivity, we can make recommendations for researchers. Finally, we compare ANCA with the other three methods and confirm that the combination of nodal attributes and network structure improves the quality of community detection. Our studies show the features of the collaboration among statisticians and present a new perspective to explore researchers.

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Section: Introductionmentioning

confidence: 99%

Community detection in attributed collaboration network for statisticians

Zhang

Pan

Wang

et al. 2023

Stat

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“…Such network data have motivated an emerging line of work that aims to deal with community detection problems that leverage both the network and the exogenous covariates. A node-coupled stochastic block model (NSBM) is proposed in [27] in which cluster information or the block matrix is uniquely encoded by the covariates. Another model from [31] specifies that the link probability between a pair of nodes is contributed additively by the block probability in an SBM and a similarity measure between the covariates of a pair of nodes.…”

Section: Introductionmentioning

confidence: 99%

Bayesian community detection for networks with covariates

Shen¹,

Amini²,

Josephs³

et al. 2022

Preprint

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The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms. Among the various learning tasks with network data, community detection, the discovery of node clusters or "communities," has arguably received the most attention in the scientific community. In many real-world applications, the network data often come with additional information in the form of node or edge covariates that should ideally be leveraged for inference. In this paper, we add to a limited literature on community detection for networks with covariates by proposing a Bayesian stochastic block model with a covariate-dependent random partition prior. Under our prior, the covariates are explicitly expressed in specifying the prior distribution on the cluster membership. Our model has the flexibility of modeling uncertainties of all the parameter estimates including the community membership. Importantly, and unlike the majority of existing methods, our model has the ability to learn the number of the communities via posterior inference without having to assume it to be known. Our model can be applied to community detection in both dense and sparse networks, with both categorical and continuous covariates, and our MCMC algorithm is very efficient with good mixing properties. We demonstrate the superior performance of our model over existing models in a comprehensive simulation study and an application to two real datasets.

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“…This suggests that vertex attributes can provide information about the network structure. Recently, a lot of works focus on attributed networks that describe not only the level of dependency but also user characteristics such as age, gender and job (see, e.g., Kossinets & Watts, 2006; Kim & Leskovec, 2012; Weng & Feng, 2016). As a result, many attributed network clustering algorithms have been proposed in order to look for an inherent assortative community structure (i.e., the type of communities wherein vertices are more likely to connect to each other if they belong to the same community), and hence, there are more edges within communities than between them (see, e.g., Chunaev, 2020; Dang & Viennet, 2012).…”

Section: Introductionmentioning

confidence: 99%

Comparison of attributed network clustering approaches to analysing Seoul public bike stations

Choi

Lee

Park

2022

Stat

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Network clustering is a fundamental task that discovers innate communities or groups in networks. Hence, network clustering methods such as spectral clustering and regularized spectral clustering have been applied in a wide range of realms. On top of a network structure, it is known in social network analysis that incorporates information from each vertex can be beneficial. This has led to the development of a series of attributed network clustering algorithms that utilize not only network connectivity but also vertex covariates in order to uncover latent clusters. This paper compares the performance of state‐of‐the‐art attributed network clustering approaches focused on detecting clusters of Seoul public bike stations. The data set consists of trip information over the bike station network in 2019. Spatial information about the bike stations is posed as vertex attributes. We show that certain attributed network clustering methods are well suited to detecting explainable clusters of bike rental stations. The results can help bike‐sharing operators better understand system usage and learn how to improve service quality in the existing system.

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Community detection with nodal information: Likelihood and its variational approximation

Cited by 15 publications

References 68 publications

Community detection in attributed collaboration network for statisticians

Community detection in attributed collaboration network for statisticians

Bayesian community detection for networks with covariates

Comparison of attributed network clustering approaches to analysing Seoul public bike stations

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