Display options
Cite
Salehi M, Rabiee HR, Rajabi A. Sampling from complex networks with high community structures. Chaos. 2012;22(2):023126doi: 10.1063/1.4712602.
Salehi, M., Rabiee, H. R., & Rajabi, A. (2012). Sampling from complex networks with high community structures. Chaos (Woodbury, N.Y.), 22(2), 023126. https://doi.org/10.1063/1.4712602
Salehi, Mostafa, et al. "Sampling from complex networks with high community structures." Chaos (Woodbury, N.Y.) vol. 22,2 (2012): 023126. doi: https://doi.org/10.1063/1.4712602
Salehi M, Rabiee HR, Rajabi A. Sampling from complex networks with high community structures. Chaos. 2012 Jun;22(2):023126. doi: 10.1063/1.4712602. PMID: 22757533.
Copy Download .nbib Format: NLM
Share it on

Chaos. 2012 Jun;22(2):023126. doi: 10.1063/1.4712602.

Sampling from complex networks with high community structures.

Chaos (Woodbury, N.Y.)

Mostafa Salehi, Hamid R Rabiee, Arezo Rajabi

Affiliations

  1. Digital Media Lab, Department of Computer Engineering, AICTC Research Center, Sharif University of Technology, Tehran, Iran. [email protected]

PMID: 22757533 DOI: 10.1063/1.4712602

Abstract

In this paper, we propose a novel link-tracing sampling algorithm, based on the concepts from PageRank vectors, to sample from networks with high community structures. Our method has two phases; (1) Sampling the closest nodes to the initial nodes by approximating personalized PageRank vectors and (2) Jumping to a new community by using PageRank vectors and unknown neighbors. Empirical studies on several synthetic and real-world networks show that the proposed method improves the performance of network sampling compared to the popular link-based sampling methods in terms of accuracy and visited communities.

Publication Types