ANF: a fast and scalable tool for data mining in massive graphs
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying sets of key players in a social network
Computational & Mathematical Organization Theory
On compressing social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
PageRank: Functional dependencies
ACM Transactions on Information Systems (TOIS)
HyperANF: approximating the neighbourhood function of very large graphs on a budget
Proceedings of the 20th international conference on World wide web
Four Degrees of Separation, Really
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Ranking experts using author-document-topic graphs
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Scalable similarity estimation in social networks: closeness, node labels, and random edge lengths
Proceedings of the first ACM conference on Online social networks
Call me maybe: understanding nature and risks of sharing mobile numbers on online social networks
Proceedings of the first ACM conference on Online social networks
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Given a social network, which of its nodes have a stronger impact in determining its structure? More formally: which node-removal order has the greatest impact on the network structure? We approach this well-known problem for the first time in a setting that combines both web graphs and social networks, using datasets that are orders of magnitude larger than those appearing in the previous literature, thanks to some recently developed algorithms and software tools that make it possible to approximate accurately the number of reachable pairs and the distribution of distances in a graph. Our experiments highlight deep differences in the structure of social networks and web graphs, show significant limitations of previous experimental results, and at the same time reveal clustering by label propagation as a new and very effective way of locating nodes that are important from a structural viewpoint.