Change Point Detection in Dynamic Networks via Regularized Tensor Decomposition | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Change Point Detection in Dynamic Networks via Regularized Tensor Decomposition

Zhang, Yuzhao - Nama Orang; Sun, Yifan - Nama Orang; Zhang, Jingnan - Nama Orang; Wang, Junhui - Nama Orang;

Dynamic network captures time-varying interactions among multiple entities at different time points, and detecting its structural change points is of central interest. This article proposes a novel method for detecting change points in dynamic networks by fully exploiting the latent network structure. The proposed method builds upon a tensor-based embedding model, which models the time-varying network heterogeneity through an embedding matrix. A fused lasso penalty is equipped with the tensor decomposition formulation to estimate the embedding matrix and a power update algorithm is developed to tackle the resultant optimization task. The error bound of the obtained estimated embedding matrices is established without incurring the computational-statistical gap. The proposed method also produces a set of estimated change points, which, coupled with a simple screening procedure, assures asymptotic consistency in change point detection under much milder assumptions. Various numerical experiments on both synthetic and real datasets also support its advantage. Supplementary materials for this article are available online.

Ketersediaan

Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art149103	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.33 No.2 June 2024
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 515-524
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: FUSED LASSO
MULTI-LAYER NETWORK
NETWORK EMBEDDING
TENSOR POWER METHOD
LATENT FACTOR MODEL
Info Detail Spesifik: https://doi.org/10.1080/10618600.2023.2240864
Pernyataan Tanggungjawab: Yuzhao Zhang, Jingnan Zhang, Yifan Sun, Junhui Wang

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