Lifting Markov chains to speed up mixing
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Fastest Mixing Markov Chain on a Graph
SIAM Review
The hitting and cover times of random walks on finite graphs using local degree information
Theoretical Computer Science
On unbiased sampling for unstructured peer-to-peer networks
IEEE/ACM Transactions on Networking (TON)
Speeding up random walks with neighborhood exploration
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Estimating and sampling graphs with multidimensional random walks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Walking on a graph with a magnifying glass: stratified sampling via weighted random walks
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Learning influence in complex social networks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Metric convergence in social network sampling
Proceedings of the 5th ACM workshop on HotPlanet
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Graph sampling via crawling has been actively considered as a generic and important tool for collecting uniform node samples so as to consistently estimate and uncover various characteristics of complex networks. The so-called simple random walk with re-weighting (SRW-rw) and Metropolis-Hastings (MH) algorithm have been popular in the literature for such unbiased graph sampling. However, an unavoidable downside of their core random walks -- slow diffusion over the space, can cause poor estimation accuracy. In this paper, we propose non-backtracking random walk with re-weighting (NBRW-rw) and MH algorithm with delayed acceptance (MHDA) which are theoretically guaranteed to achieve, at almost no additional cost, not only unbiased graph sampling but also higher efficiency (smaller asymptotic variance of the resulting unbiased estimators) than the SRW-rw and the MH algorithm, respectively. In particular, a remarkable feature of the MHDA is its applicability for any non-uniform node sampling like the MH algorithm, but ensuring better sampling efficiency than the MH algorithm. We also provide simulation results to confirm our theoretical findings.