Randomized algorithms
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Journal of Computational and Applied Mathematics
Proceedings of the 20th international conference on World wide web
Quick detection of top-k personalized pagerank lists
WAW'11 Proceedings of the 8th international conference on Algorithms and models for the web graph
Online sampling of high centrality individuals in social networks
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Online estimating the k central nodes of a network
NSW '11 Proceedings of the 2011 IEEE Network Science Workshop
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Our goal is to quickly find top k lists of nodes with the largest degrees in large complex networks. If the adjacency list of the network is known (not often the case in complex networks), a deterministic algorithm to find the top k list of nodes with the largest degrees requires an average complexity of $\mbox{O}(n)$, where n is the number of nodes in the network. Even this modest complexity can be very high for large complex networks. We propose to use the random walk based method. We show theoretically and by numerical experiments that for large networks the random walk method finds good quality top lists of nodes with high probability and with computational savings of orders of magnitude. We also propose stopping criteria for the random walk method which requires very little knowledge about the structure of the network. Õ(n2)