Brief announcement: revisiting the power-law degree distribution for social graph analysis

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 Powerlaw 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 shortestpath prediction, community detection, and influence maximization. We provide a formal proof of the error reduction using the ParetoLognormal distribution, which we envision will have strong implications on the correctness of social systems and applications. Categories and Subject Descriptors I.6.4 [Simulation and Modeling]: Model Validation and Analysis;...

• Type: Conference
• Year: 2010
• Where: PODC
• Authors: Alessandra Sala, Haitao Zheng, Ben Y. Zhao, Sabrina Gaito, Gian Paolo Rossi
• Keywords: Distributed And Parallel Computing, Node Degree Distribution, PODC 2010, Power-law Distribution, Social Networks