4.6 Article

Vicinal Vertex Allocation for Matrix Factorization in Networks

Journal

IEEE TRANSACTIONS ON CYBERNETICS
Volume 52, Issue 8, Pages 8047-8060

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3051606

Keywords

Computational modeling; Correlation; Data models; Task analysis; Probabilistic logic; Network topology; Adaptation models; Community detection; complex network; graph clustering; network clustering; probabilistic matrix factorization; vicinal vertex-allocated matrix factorization (VVAMo)

Funding

  1. National Research Foundation, Singapore under its AI Singapore Programme (AISG) [AISG-RP-2018-004]
  2. Data Science Artificial Intelligence Research Centre, Nanyang Technological University
  3. Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU) - Singapore Government through the Industry Alignment Fund-Industry Collaboration Projects Grant

Ask authors/readers for more resources

The VVAMo model effectively uncovers clusters in network data by considering the latent preferences between vicinal vertices, achieving superior performances compared to existing approaches.
In this article, we present a novel matrix-factorization-based model, labeled here as Vicinal vertex allocated matrix factorization (VVAMo), for uncovering clusters in network data. Different from the past related efforts of network clustering, which consider the edge structure, vertex features, or both in their design, the proposed model includes the additional detail on vertex inclinations with respect to topology and features into the learning. In particular, by taking the latent preferences between vicinal vertices into consideration, VVAMo is then able to uncover network clusters composed of proximal vertices that share analogous inclinations, and correspondingly high structural and feature correlations. To ensure such clusters are effectively uncovered, we propose a unified likelihood function for VVAMo and derive an alternating algorithm for optimizing the proposed function. Subsequently, we provide the theoretical analysis of VVAMo, including the convergence proof and computational complexity analysis. To investigate the effectiveness of the proposed model, a comprehensive empirical study of VVAMo is conducted using extensive commonly used realistic network datasets. The results obtained show that VVAMo attained superior performances over existing classical and state-of-the-art approaches.

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