Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast shortest path distance estimation in large networks
Proceedings of the 18th ACM conference on Information and knowledge management
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The study of complex networks led to the belief that the connectivity of network nodes generally follows a Power-law distribution. In this work, we show that modeling large-scale online social networks using a Power-law distribution produces significant fitting errors. We propose the use of a more accurate node degree distribution model based on the Pareto-Lognormal distribution. Using large datasets gathered from Facebook, we show that the Power-law curve produces a significant over-estimation of the number of high degree nodes, leading researchers to erroneous designs for a number of social applications and systems, including shortest-path prediction, community detection, and influence maximization. We provide a formal proof of the error reduction using the Pareto-Lognormal distribution, which we envision will have strong implications on the correctness of social systems and applications.