Variational Approach for Learning Community Structures
Joint Authors
Choong, Jun Jin
Liu, Xin
Murata, Tsuyoshi
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-13
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Discovering and modeling community structure exist to be a fundamentally challenging task.
In domains such as biology, chemistry, and physics, researchers often rely on community detection algorithms to uncover community structures from complex systems yet no unified definition of community structure exists.
Furthermore, existing models tend to be oversimplified leading to a neglect of richer information such as nodal features.
Coupled with the surge of user generated information on social networks, a demand for newer techniques beyond traditional approaches is inevitable.
Deep learning techniques such as network representation learning have shown tremendous promise.
More specifically, supervised and semisupervised learning tasks such as link prediction and node classification have achieved remarkable results.
However, unsupervised learning tasks such as community detection remain widely unexplored.
In this paper, a novel deep generative model for community detection is proposed.
Extensive experiments show that the proposed model, empowered with Bayesian deep learning, can provide insights in terms of uncertainty and exploit nonlinearities which result in better performance in comparison to state-of-the-art community detection methods.
Additionally, unlike traditional methods, the proposed model is community structure definition agnostic.
Leveraging on low-dimensional embeddings of both network topology and feature similarity, it automatically learns the best model configuration for describing similarities in a community.
American Psychological Association (APA)
Choong, Jun Jin& Liu, Xin& Murata, Tsuyoshi. 2018. Variational Approach for Learning Community Structures. Complexity،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1134406
Modern Language Association (MLA)
Choong, Jun Jin…[et al.]. Variational Approach for Learning Community Structures. Complexity No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1134406
American Medical Association (AMA)
Choong, Jun Jin& Liu, Xin& Murata, Tsuyoshi. Variational Approach for Learning Community Structures. Complexity. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1134406
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134406