Efficient crawling through URL ordering
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Breadth-first crawling yields high-quality pages
Proceedings of the 10th international conference on World Wide Web
Adaptive on-line page importance computation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Sampling large Internet topologies for simulation purposes
Computer Networks: The International Journal of Computer and Telecommunications Networking
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Metropolis Algorithms for Representative Subgraph Sampling
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Quick detection of nodes with large degrees
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
Estimating sharer reputation via social data calibration
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast approximation of betweenness centrality through sampling
Proceedings of the 7th ACM international conference on Web search and data mining
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In this work, we investigate the use of online or “crawling” algorithms to sample large social networks in order to determine the most influential or important individuals within the network (by varying definitions of network centrality). We describe a novel sampling technique based on concepts from expander graphs. We empirically evaluate this method in addition to other online sampling strategies on several real-world social networks. We find that, by sampling nodes to maximize the expansion of the sample, we are able to approximate the set of most influential individuals across multiple measures of centrality.