Monte Carlo Algorithms for Identifying Densely Connected Subgraphs | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Monte Carlo Algorithms for Identifying Densely Connected Subgraphs

Zhang, Jingfei - Nama Orang; Chen, Yuguo - Nama Orang;

The problem of finding densely connected subgraphs in a network has attracted a lot of recent interest. Such subgraphs are sometimes referred to as communities in social networks or molecular modules in protein networks. In this article, we propose two Monte Carlo optimization algorithms for identifying the densest subgraphs with a fixed size or with size in a given range. The new algorithms combine the idea of simulated annealing and efficient moves for the Markov chain, and both algorithms are shown to converge to the set of optimal states (densest subgraphs) with probability 1. When applied to a yeast protein interaction network and a stock market graph, the algorithms identify interesting new densely connected subgraphs. Supplementary materials for the article are available online.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art101639	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.24 No.3 September 2015
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 827-845
Bahasa: English
ISBN/ISSN: -
Klasifikasi: -
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: NETWORK
SIMULATED ANNEALING
GLOBAL OPTIMIZATION
DENSEST SUBGRAPH DISCOVERY
QUASICLIQUE
Info Detail Spesifik: https://doi.org/10.1080/10618600.2014.930040
Pernyataan Tanggungjawab: Jingfei Zhang, Yuguo Chen

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