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Autonomous Overlapping Community Detection in Temporal Networks: A Dynamic Bayesian Nonnegative Matrix Factorization Approach.

Wang, W., Jiao, P., He, D., Jin, D., Pan, L. and Gabrys, B., 2016. Autonomous Overlapping Community Detection in Temporal Networks: A Dynamic Bayesian Nonnegative Matrix Factorization Approach. Knowledge-Based Systems, 110, 121-134.

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KBS_Wang_et_al_Overlapping_Communities_in_Temporal_Networks_2016_accepted.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1016/j.knosys.2016.07.021


A wide variety of natural or artificial systems can be modeled as time-varying or temporal networks. To understand the structural and functional properties of these time-varying networked systems, it is desirable to detect and analyze the evolving community structure. In temporal networks, the identified communities should reflect the current snapshot network, and at the same time be similar to the communities identified in history or say the previous snapshot networks. Most of the existing approaches assume that the number of communities is known or can be obtained by some heuristic methods. This is unsuitable and complicated for most real world networks, especially temporal networks. In this paper, we propose a Bayesian probabilistic model, named Dynamic Bayesian Nonnegative Matrix Factorization (DBNMF), for automatic detection of overlapping communities in temporal networks. Our model can not only give the overlapping community structure based on the probabilistic memberships of nodes in each snapshot network but also automatically determines the number of communities in each snapshot network based on automatic relevance determination. Thereafter, a gradient descent algorithm is proposed to optimize the objective function of our DBNMF model. The experimental results using both synthetic datasets and real-world temporal networks demonstrate that the DBNMF model has superior performance compared with two widely used methods, especially when the number of communities is unknown and when the network is highly sparse.

Item Type:Article
Uncontrolled Keywords:Community detection; Temporal networks; Bayesian nonnegative matrix factorization; Gradient descent; Model selection
Group:Faculty of Science & Technology
ID Code:24683
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:01 Sep 2016 15:49
Last Modified:27 Jul 2017 01:08


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